Driven by accelerated product obsolescence and frequent consumer replacements, electronic waste is growing rapidly. Waste recycling, as a core component of resource reuse, has become an important means of alleviating resource scarcity and reducing environmental pollution. In the process of recycling discarded products, the efficiency of disassembly operations is crucial. To improve disassembly efficiency and maximize resource utilization, this work proposes a hybrid disassembly line structure that incorporates both linear and U-shaped workstations. Shared labor is introduced between adjacent disassembly lines, allowing workers to flexibly execute tasks across lines. This resource-sharing mechanism enhances task coordination and reduces idle time, contributing to improved system efficiency. Using a precedence relationship graph to model dependencies among tasks, we develop a mathematical model aimed at maximizing profit. We use an exact solver to verify the model and adopt a variant of dueling deep Q-network, called PER-Dueling DQN (PDDQN), which incorporates prioritized experience replay to enhance sampling efficiency and solve the model optimally. A simulation environment aligned with this problem is constructed for the reinforcement learning agent. We compare the proposed method with other reinforcement learning approaches, including advantage actor-critic, proximal policy optimization, and trust region policy optimization. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of PDDQN are demonstrated, exhibiting significant advantages over other methods.

Industry 5.0 promotes the transformation of manufacturing toward flexibility, personalization, and sustainability. As a critical component of closed-loop manufacturing systems, disassembly operations urgently require more flexible and efficient human–robot collaboration models. To this end, this work, for the first time, proposes a multihuman–robot collaborative circular disassembly line balancing problem. By allowing workers to move between robotic workstations, the proposed system enhances operational flexibility. Furthermore, a multiworker mechanism is introduced to improve fault tolerance and system stability, overcoming the limitations of fixed worker positions in existing collaborative disassembly research. To solve this problem, we formulate a discrete-time mixed-integer programming model based on product AND/OR graphs, aiming to maximize disassembly profit. The model’s correctness is verified using CPLEX. Additionally, we develop a heterogeneous graph neural network-enhanced proximal policy optimization (PPO) algorithm. By integrating product and workstation information into a heterogeneous graph, the algorithm performs two-stage feature extraction and node embedding via graph neural networks. Based on these embeddings, the agent dynamically selects multiple actions per decision step to simulate the behavior of multiple workers moving simultaneously. Experimental results show that the proposed method outperforms traditional reinforcement learning algorithms such as PPO and deep Q-network algorithm in terms of disassembly profit. Moreover, it demonstrates strong generalization capability in cross-task transfer and scalability experiments involving different task graph sizes. The improved performance is achieved with acceptable computational time.

Disassembly is a critical step in the recycling and reusing of end-of-life products. As Industry 5.0 emerges, manufacturing is shifting from a system-oriented approach to a human-centered paradigm, advancing human–robot collaboration to a new stage. However, existing studies on human–robot collaboration in disassembly lines generally overlook the mobility of workers. To fill the research gap, this work proposes a novel human–robot collaboration mode that considers both the mobility of workers during disassembly and the flexibility of collaboration time in human–robot interaction. Based on this model, this work proposes the human-assisted robotic circular disassembly line balancing problem and establishes a profit-oriented spatiotemporal decomposition mixed-integer programming model. A two-stage greedy proximal policy optimization algorithm is designed to solve it. To validate the effectiveness of the proposed model and algorithm, ten sets of benchmark instances are generated with different scales based on real product structure data. Comparative experiments with reinforcement learning algorithms and classical heuristic methods demonstrate the feasibility and significant superiority of the proposed algorithm in solving this type of problem.

Electric load inherently reflects the collective patterns of social activities and user behaviors, making their accurate prediction a challenging task. Accurate electric load forecasting is crucial for the planning, operation, scheduling, and market management of modern power systems, especially under the increasing complexity of residential energy consumption behaviors. From a data-driven modeling perspective, traditional load forecasting based solely on time-series data often fails to capture the social and behavioral dimensions underlying demand fluctuations. To address these challenges, this work presents an innovative electric load forecasting approach by using multifactor and time-series forecasting concepts. A comprehensive feature pool is first constructed by combining social and environmental factors, feature decomposition, and basis function transformation. Then, a metaheuristic-enhanced feature selection and modeling framework is proposed, which leverages a simulated annealing (SA) algorithm in conjunction with a hybrid deep learning architecture. Specifically, it encodes selected features as a solution of SA and evaluates it by a hybrid deep learning model that incorporates an attention mechanism, convolutional neural networks, and long short-term memory networks. In this way, it can effectively capture both temporal dependencies and social-behavioral influences on load patterns. The proposed approach is validated on 26 real-world datasets of residential electric load, which reveals that forecasting performance directly reflects aggregated social behavior in energy usage. Their synergistic effect achieves a maximum$\boldsymbol{R^{2}}$of 0.97 with a prediction error margin of less than 5% and enables the proposed approach to outperform several state-of-the-art peers. These results highlight the value of integrating social system factors with computational intelligence, showcasing the potential of the proposed method for practical applications in electric load forecasting.

A share-a-ride problem (SARP), which integrates the transportation of both passengers and parcels by the ride-hailing platforms such as Uber and Lyft, has drawn considerable attention. This work introduces a novel share-a-ride problem with flexible pick-up and drop-off points (SARP-FUO) with the objectives of maximizing the total revenue of the ride-hailing platforms and minimizing the total travel distance of vehicles. A mixed integer programming model is developed to formulate SARP-FUO. Then, a knowledge-based multi-objective brain storm optimization algorithm (KM-BSO) is proposed to solve it. Two knowledge-based local search operators are specifically designed to enhance the exploration capability of KM-BSO for identifying potential nondominated solutions. The first operator employs a dynamic programming algorithm to readjust pick-up and drop-off points, while the second modifies vehicle routes based on four derived properties. Extensive experiments are conducted to compare KM-BSO with nondominated sorting genetic algorithm II, multi-objective evolutionary algorithm based on decomposition, multi-objective artificial bee colony algorithm, and a mathematical programming solver CPLEX. The results and statistical analysis demonstrate the superiority of the proposed approach in solving the studied problem. Finally, a sensitivity analysis is performed with and without flexible pick-up and drop-off points, demonstrating the advantages of the proposed model in developing intelligent public transportation systems.

As the demand for disassembling end-of-life products grows, limitations in traditional disassembly line design, low efficiency, and high resource consumption become increasingly evident. Particularly in large-scale disassembly tasks, where the cost of conventional remanufacturing rises and the technologies fail to meet high-efficiency requirements. The integration of robots into disassembly lines is a promising solution to alleviate these issues. This work presents a multi-product hybrid disassembly line balancing problem that considers machine wear rates and establishes a mixed-integer programming model guided by profit maximization to address it. An improved fireworks algorithm is used in the proposed approach. The developed solution is compared with genetic and ant colony algorithms. Evaluation results and analysis demonstrated the competitive efficiency and stability of our approach.

In recent years, with the rapid development of artificial intelligence and social media, the influence of word-of-mouth (WOM) on the diffusion of online content has become increasingly evident. Video platforms can use artificial intelligence to collect WOM data of programs and formulate corresponding pricing strategies. Based on this background, considering the impact of online WOM effects on the diffusion of on-demand content exclusive to members, this study constructs a two-stage product provision model for online video platforms, consisting of the premiere and follow-up broadcast stage. Based on expected utility theory, this research explores the pricing strategies for member-exclusive on-demand content under two profit models and analyzes the influence of program WOM attributes and program quality on optimal decision-making. The findings reveal that: When the premiere stage WOM for a program is either highly positive or negative, video platform should adopt an "advertising-dominant strategy". When the premiere stage WOM is moderate, a " fee-dominant strategy" is preferable. Higher program quality increases the platform's inclination toward the "fee-dominant strategy". The better the premiere stage WOM and program quality, the more users tend to watch during the premiere stage. Accordingly, both the program price and the platform's expected profit will vary to different degrees depending on these conditions.

The disassembly and assembly line balancing problem (DALP) is a critical task in industrial production, involving the efficient organization of disassembly and assembly tasks to improve the productivity and flexibility of production lines. In practical applications, task allocation, robot movement, and workstation layout optimization are key factors affecting production efficiency. This study proposes an improved parallel advantage actor-critic algorithm to address DALP with space constraints due to robot movement. Considering the limitations of workstation space, this approach optimizes the robot's movement paths between workstations, reducing the cost of opening workstations, and optimizing task allocation strategies. To enhance the convergence speed and stability of the conventional Parallel A2C algorithm, action space optimization and a greedy strategy are incorporated into the algorithm. Experimental results demonstrate that the improved parallel advantage actor-critic outperforms the A2C and AC algorithms in terms of efficiency and performance, particularly in handling disassembly tasks with space constraints, significantly improving the operational efficiency and economic benefits of the production line.

Product disassembly is significant for recycling scrapped products and reducing environmental pollution and resource waste. The recovery, reuse, and recycling of industrial products is crucial in modern industry. Manual disassembly efficiency significantly impacts the disassembly line’s overall effectiveness, especially workers’ skill level and learning efficiency. This paper proposes a multi-period personnel scheduling problem that considers worker learning effects. A mixed integer programming model for the disassembly balance problem was established to maximize disassembly profit. This problem is solved using a new reinforcement learning algorithm, the importance-weighted actor-learner architecture (IMPALA). The correctness and effectiveness of the proposed algorithm are verified through comparative experiments with the famous IBM optimizer CPLEX and some popular peer algorithms.

The advances of manufacturing technology accelerates the replacement of consumer products. The recycling of these out-of-date products not only has economic benefits but also contributes to environmental protection. Therefore, the disassembly and reuse of products have attracted great attention all over the world. The traditional human worker disassembly is characterized by high cost and low efficiency. Robots can work more efficiently, but they are not flexible enough to perform different tasks. On the other hand, the combination of a U-shaped disassembly line and a single-row linear disassembly line would offer unique advantages for various applications. This work studies a hybrid disassembly line balancing problem (HDLBP) based on human-robot collaboration. The special challenge with HDLBP is that we need to consider the work load balancing among different lines, in addition to workstations, to achieve optimal results. A combination of linear programming and integer one is proposed to solve the optimization model of HDLBP that is composed of linear and U-shaped disassembly lines, with the objective of maximal disassembly profit. The feasibility of the model is verified by commercial solver CPLEX in solving different size problem instances. Note to Practitioners—This work deals with issue of using human workers only or using robots alone in disassembly lines and the limitation of each type of disassembly line layout. Most of the existing disassembly operation assignment methods are based on the correlation between humans and robots and the factors that affect disassembly. This paper suggests that the selection of humans and robots based on an optimization model that can be solved CPLEX. To leverage the unique advantages offered by each type of disassembly layout, this paper suggests the use of hybrid disassembly lines. Based on the idea of mixed integer programming, a hybrid disassembly line model of human-robot collaboration is designed and solved by CPLEX. The experimental results show that the hybrid disassembly line of human-robot collaboration has obvious advantages over the disassembly line composed of worker-only or robot-only when disassembling products. In the future research, we will use reinforcement learning algorithm to solve the hybrid disassembly line balancing problem, and consider more details of the human-robot cooperative hybrid disassembly lines.

Slab yards serve as temporary slab storage between a continuous casting stage and a rolling stage. Considering non-crossing and safe clearance constraints of slab yard cranes, this work studies a multi-crane assignment and scheduling problem in the slab yard. An mixed-integer linear programming (MILP) is formulated to minimize the slab completion time. Due to its NP-hardness, the problem for large-sized instances is computationally intractable. Thus, we develop a logic-based benders decomposition algorithm (LBBD) to solve it. First, we exploit a generalized decomposition of this problem into a relaxed main problem (RMP) and a sub-problem (SP). Solving the former allocates slabs to each crane. Then, the sequence of the assigned slabs can be found by solving its corresponding sub-problem. Finally, to verify the effectiveness of LBBD, we identify a lower bound (LB) of the optimal objective function. The problem instances on real data from an iron and steel plant are created. The result of LBBD is close to such lower bound and can be found efficiently. Note to Practitioners—This work deals with a crane assignment problem with multiple cranes for handling input slabs in a slab yard. This problem is formulated as an MILP model to minimize the completion time. Its time complexity grows exponentially with the problem size. Thus, we develop a LBBD to solve it. The numerical results reveal that LBBD can find the optimal or near-optimal solution for all realistic instances in affordable computational time. Its use can ensure the high utilization of cranes and efficient service in iron and steel plants.

The escalating consumption and disposal of electronic products have spurred a pressing demand for environmental conservation. Traditional disassembly factories encounter challenges when handling discarded products from various locations, including high costs and limited flexibility. This study addresses a multifactory disassembly process optimization problem, taking into account worker posture and the selection of disassembly line types. Subsequently, a mathematical model to maximize profit is built. The reinforcement learning algorithm, Categorical deep Q network (DQN), is utilized to find optimal solutions. Experimental results are compared with those from CPLEX to validate the precision and viability of the proposed model. Furthermore, we compare the proposed solution with various reinforcement learning algorithms, including DQN, proximal policy optimization, and Advantage Actor–Critic. The effectiveness of the proposed model and algorithm is verified by experiments on several cases with different complexity scales.

Efficient disassembly is essential for the reintegration of end-of-life products into the remanufacturing process. Previous studies utilize human–robot collaboration and parallel workstations to enhance disassembly efficiency. However, the disassembly lines in these studies are typically independent of each other. As the number of disassembly lines in a plant increases, labor resources such as workers and robots become redundant, leading to low resource utilization and decreased disassembly revenue. This study proposes a novel disassembly scheme aimed at achieving high efficiency by leveraging parallelization and human–robot collaboration to share labor resources on a hybrid disassembly line. Specifically, this work develops a mixed-integer programming model to maximize disassembly profit. A discrete aquila optimizer algorithm, incorporating uniform variation and two-point crossover methods, provides the solution for the problem. Furthermore, the correctness of the proposed model and algorithm is verified within the solvable range of the commercial solver CPLEX. Finally, a comparative analysis of the proposed algorithm with the salp swarm algorithm, the fireworks algorithm, and the whale optimization algorithm demonstrates its superiority in solving the problem.

To reduce resource consumption and environmental impact, the manufacturing industry increasingly leans towards repurposing, repairing, or updating products. In a multifactory environment, considering the disassembly line balancing problem helps enterprises improve production efficiency and reduce costs. Thus, this work proposes a heterogeneous multifactory remanufacturing optimization problem, considering the disassembly techniques and U-shaped disassembly lines that are used in heterogeneous disassembly factories. A mixed integer programming model for profit maximization is established. Reinforcement learning methods open new avenues for addressing complex scheduling issues in actual production. This article utilizes the twin delayed deterministic policy gradient algorithm to solve the proposed problem. It validates the effectiveness of the algorithm by comparing it with CPLEX. Through various experimental cases, it demonstrates that this method achieves better convergence and higher profits compared to deep deterministic policy gradient, soft actor-critic, and advantage actor-critic algorithms.

The advancement of human–robot collaboration technology has positioned remanufacturing as a crucial part of the circular economy, driving both economic growth and environmental sustainability. In the era of Industry 5.0, these technologies enhance the efficiency and flexibility of disassembly tasks. However, most research on human–robot collaborative disassembly (HRCD) line balancing overlooks the mobility of workers. This study introduces a profit-oriented HRCD model incorporating mobile workers. To address large-scale HRCD challenges, it proposes a dynamic attraction rate mechanism that improves the traditional carnivorous plant algorithm (CPA), tackling issues of slow convergence and local optimization. The experimental framework includes three validation phases: 1) comparison with the exact solver IBM ILOG CPLEX Optimization Studio (CPLEX); 2) parameter sensitivity analysis; and 3) benchmarking against seven state-of-the-art algorithms. Results demonstrate that HRCD with mobile workers significantly boosts disassembly efficiency and reduces disassembly time compared to traditional methods. Additionally, it increases profits through flexible task allocation. In cases of incomplete disassembly, HRCD with mobile workers yields an average benefit increase of 87.64% over conventional disassembly modes. A comparative evaluation with other swarm intelligence algorithms further highlights the superior solution quality and time efficiency of the improved CPA.

Multifactory remanufacturing is a widely adopted sustainable manufacturing approach nowadays. Its complexity lies in coordinating the dismantling, remanufacturing, and resource circulation among factories to maximize resource reuse and minimize environmental impact. Proper worker scheduling is crucial in this process to ensure efficient workflow and maximal resource utilization. This study proposes and addresses an optimization problem for multifactory remanufacturing considering worker scheduling, which is mainly divided into two parts: worker scheduling and remanufacturing process optimization (MRPO). A mixed-integer programming (MIP) mathematical model is established with the objective of profit maximization. A discrete battle royale optimization (BRO) algorithm is proposed to solve this problem, with a novel encoding structure and three soldier search strategies devised to better search for the optimal solution. The correctness of the model is validated through experiments on cases of different scales and comparisons with the IBM CPLEX optimizer. Furthermore, comparisons with the carnivorous plant algorithm (CPA), whale optimization algorithm (WAO), dingo optimization algorithm, and migrating bird optimization algorithm demonstrate the superiority and effectiveness of the proposed algorithm.

Ecological environment and natural resource issues are becoming more and more prominent, which promotes the recycling of waste products for green economy. Disassembly plays a key role in the remanufacturing and reuse of waste products. However, with the rapid development of production automation, designers tend to ignore the fact that manual operation is more flexible. It is of great importance to consider human factors in a disassembly process. This work considers two human disassembly postures, namely standing and sitting. The multiobjective disassembly line balancing problem considering human posture changes is studied. A mathematical model with the objective functions of maximizing profit, minimizing the number of posture changes at a workstation, and minimizing the difference of maximum posture changes between any two workstations is established. The model is solved through a newly proposed Pareto-based discrete harmony search algorithm. Three neighborhood structures are designed to enlarge the search space for better solutions. Furthermore, an elite reserve strategy is used to improve the global optimization ability of the proposed algorithm. Finally, the proposed model and algorithm are applied to cases of different scales of complexities, and the effectiveness of the proposed model and algorithm is verified in comparison with four competitive algorithms.

With the advancement of vehicular automation and communication technology, autonomous driving has emerged as a significant trend in future transportation. Robotaxis, an innovative mode of transportation that integrates robotics and artificial intelligence, are anticipated to become widely used, thereby revolutionizing urban mobility. This work proposes a multi-objective mixed integer programming model for robotaxi dispatch. Unlike previous approaches, it can recommend passengers’ pick-up points, drop-off points, and boarding time (BT) that may deviate from their initial origins, destinations, and BT, respectively. It encourages passengers to accept the recommended pick-up and drop-off (UO) points or to be picked up slightly earlier or later. The objectives are to maximize the profit per kilometer of robotaxis and to minimize the total travel expense of passengers. Subsequently, a nondominated sorting genetic algorithm with mass center (NSGA-MC) is proposed to solve the model. It outperforms nondominated sorting genetic algorithm II (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) across several metrics. Some instances provide detailed results that illustrate the effectiveness of the proposed algorithm. Additionally, a sensitivity analysis is performed, comparing scenarios with and without UO-point and boarding-time recommendations. An experiment is also conducted to examine various recommendation acceptance rates, demonstrating the advantages of the proposed model in developing intelligent public transportation systems.

Autonomous driving has been successfully implemented in such particular areas as logistics distribution centers, container terminals, and university campuses. Robotaxi represents one of its important applications. This work studies a robotaxi dispatch problem during the pandemic era. It aims to design a robotaxi dispatch approach according to a defined severity degree of the pandemic, which can decrease a virus infection rate by reducing contact among passengers. It develops a multi-objective optimization model to minimize travel cost of robotaxis, waiting time of both robotaxis and passengers, and contact among passengers. A two-stage nondominated sorting genetic algorithm (NSGA-TS) is proposed to solve the problem. Three operations are designed to generate new solutions, which can ensure its solution diversity and speed up its convergence. Its effectiveness is verified via its comparison with two popular multi-objective optimization algorithms, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D) and nondominated sorting genetic algorithm II (NSGA-II). Experimental results show that the proposed model can effectively reduce travel cost and waiting time. Besides, it can reduce the virus infection rate by decreasing contact among passengers at different severity degrees of the pandemic. This work is conducive for our society to building intelligent transportation systems in the post-pandemic era.

With the rapid development of information technology and logistics technology, traditional centralized factories are transforming into distributed production systems, forming a multi-factory manufacturing model. This study uses Petri nets to model the disassembly processes of end-of-life (EOL) products, integrates the disassembly line balancing issue with the resource sharing over multiple factories, propose a hybrid layout for multi-factory remanufacturing, and establishes a linear programming mathematical model that optimizes the disassembly profit. Deep deterministic policy gradient(DDPG), a deep reinforcement learning algorithm, is employed to solve the model. Experimental results demonstrate the feasibility of the proposed approach.

Freeway bottlenecks caused by traffic incidents contribute significantly to large-scale traffic congestion. Traditional strategies, including variable speed limit (VSL) and ramp metering, are commonly used for freeway traffic congestion management. Recently, vehicle platooning has become a promising way to alleviate traffic bottlenecks. This work proposes a novel framework that combines VSL and vehicle platooning for freeway bottleneck, referred to as VSL-VP, in mixed traffic of connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). First, the upstream road of a bottleneck is divided into two segments, called the former and the latter. VSL limits vehicle speed at the former segment, thereby reducing inflow traffic to the latter one. Then, deep reinforcement learning is employed for CAV platooning at the latter segment, where low traffic flow density and large car-following distance create conditions for smooth lane change and platoon formulation of CAVs. Simulation results demonstrate that VSL-VP significantly enhances the bottleneck throughput and reduces traffic congestion at elevated levels of CAV penetration rates.

Trip recommendation aims to provide users with a sequence of points of interest (POIs) according to their interests and requirements when exploring unfamiliar cities. In contrast to prior research on trip recommendation, our research deals with such a problem: If a user is scheduled to attend an academic conference at 2:30 PM, how might he/she make a visit to the city's attractions while still managing to attend the conference? To address this problem, a trip recommendation method based on mixed graph representation learning is proposed. Firstly, a mixed graph is used to describe the spatial temporal, and transition knowledge in users' check-in data. Then, we employ the graph convolutional network to integrate knowledge matrices extracted from the mixed graph. Finally, a trip inference module, which incorporates a dual decoder, POI popularity knowledge, and positional encoding, is designed to generate a trip for a given query. Experiments are conducted on five real-world trip datasets. The results demonstrate that the proposed method outperforms several widely-used baselines when recommending a trip with an FP.

Point of interest (POI) recommendation is a hot research topic. Current researches mainly focus on the analysis of personal check-in trajectories to obtain user preferences. However, a user's check-in data is generally sparse, and it is difficult to make accurate recommendation by only using the user's local information. Additionally, the public's check-in behavior may exhibit common visiting patterns, and incorporating global check-in information is beneficial for enhancing the learning of individual user preferences. Therefore, we propose a GCN-based model with Fusion of Global and Local information (GFGL) for the next POI recommendation. The model obtains global information such as spatial distance, social relationships., and transition probabilities from all users' visit trajectories, and utilizes graph convolution network (GCN) for learning of multi-dimensional global information. Next, we fuse global information with user local information through the user context information embedding module. Besides., a long short-term memory (LSTM) model and transformer model are used to learn the relationship between the user's sequential preference and non-adjacent visits in trajectories. Extensive experiments on two real-world datasets demonstrate the superiority of GFGL against state-of-the-art methods in the next POI recommendation.

In location-based services (LBS), trip recommendation accuracy is challenged by diverse user preferences and complex transfer behaviors. Previous studies overlook the reverse effect of following POIs on previously visited ones. To address this, we propose a Graph-based Double-layer Bidirectional Trip Recommendation (GDB-TR) model. This model uses a heterogeneous graph to model user check-in trajectories with spatial and temporal information. Subgraphs are extracted from the heterogeneous graph, and an adjacency matrix is built for each subgraph. These matrices are fused through a neural network to obtain vector representations for POIs and POI categories. GDB-TR's core is a double-layer bidirectional neural network: one layer describes POIs, the other POI categories. Bidirectional computation captures the influence of preceding POIs on following ones and vice versa. Experiments on five real-world datasets demonstrate GDB-TR's superiority over baseline models, measured by${\boldsymbol{F}}_{\boldsymbol{{1}}}$and pairs-${\boldsymbol{F}}_{\boldsymbol{{1}}}$metrics.

With the fast progress in autonomous driving and communication technologies, robotaxis emerge as a novel mode of transport. Optimization of robotaxi dispatch with ride-sharing can enrich the travel choices of residents and improve the network capacity of transportation systems. This work proposes a multi-objective optimization model for robotaxi dispatch. Unlike previous approaches, this is the first attempt to adjust simultaneously unreasonable boarding time (BT) and pick-up/drop-off (UO) points for passengers during the dispatch. It encourages passengers to walk to the recommended UO points or to be picked up slightly earlier or later, which aims to maximize the profit per kilometer of robotaxis and to minimize the total travel expense of passengers. Consequently, a nondominated sorting genetic algorithm with mass center (NSGA-MC) is proposed to address the model. Experimental results show that the proposed algorithm outperforms its peers from multiple metrics, which highlight the advantage in advancing intelligent public transportation systems.

The development of Connected and Automated Trucks (CATs) provides a new opportunity for freight industry to enhance fuel efficiency, increase traffic flow, and improve safety through platooning. Particularly at highway on-ramps, how to effectively form CAT platoons is a key research topic. In the process of CAT platooning, the timing, location, and speed of CAT merging significantly impact safety and energy consumption. Thus, this study proposes a hierarchical merging strategy, aimed at achieving effective autonomous CAT platooning at highway on-ramps by considering the interference of human-driven vehicles (HDVs). Specifically, we employ a model-free deep reinforcement learning method that guides CAT merging process by exploring optimal driving behaviors. It ensures the safety and efficiency of the CAT merging process. In addition, we use the real vehicle dynamics model in simulation. The proposed strategy can handle the variation of the CATs' initial positions and speeds at on-ramps, as well as interference caused by HDVs at highway mainline. The effectiveness of the proposed strategy has been validated through simulations. The results show that the proposed strategy can effectively coordinate CAT platooning at highway on-ramps.

Traffic congestion frequently occurs on slope segments of highways, which is a typical bottleneck. With the development of connected and autonomous vehicle (CAV) technology, there will be a scene that CAVs and human driven vehicles (HDVs) co-exist. This work studies a slope bottleneck on highway in mixed traffic scenarios and proposes a traffic flow model for slope bottlenecks incorporating CAV platooning based on cellular automata. A novel traffic flow control strategy for slope bottlenecks is proposed based on variable speed limit (VSL) and vehicle platooning. Firstly, it divides the upstream section of the slope bottleneck into two zones for implementing VSL and vehicle platooning, respectively. Via speed restrictions within the VSL zone, the inflow of vehicles into the vehicle platooning zone is effectively mitigated to create low traffic density. In the vehicle platooning zone, a hybrid vehicle platooning method for mixed scenarios is proposed. The experimental results demonstrate that our strategy effectively enhances traffic flow of the slope bottleneck, thereby mitigating traffic congestion.

Ship operation time is a crucial factor in developing berth plans. While most existing research focuses on container terminals, few scholars have examined bulk cargo terminals due to the unique nature of their cargo and complex operational processes. This work proposes a berth characteristic classification-based prediction method (BCCPM) to predict the operation time of ships in bulk cargo terminals. Firstly, recognizing both the similarities and differences among berths in bulk cargo terminals, this paper introduces a berth clustering method based on K-means to group berths with similar operational traits. Then, to conquer the limitations of individual machine learning models in prediction, a stacking ensemble learning approach is proposed for predicting ship operation time in various types of berths. Experiments are conducted on the real operation data from Qingdao dry bulk cargo terminal, China. The results show that outperforms single machine learning models in terms of prediction accuracy and generalization. Moreover, BCCPM effectively captures operational nuances of different berths, resulting in a 2-hour reduction in MAE and a 6% decrease in MAPE compared to the overall prediction method.

Product disassembly is critically important in recycling end-of-life products, reducing their negative impact on environmental pollution and minimizing resource waste. Disassembly line balancing problems have attracted much attention from researchers and industrial practitioners. Most of the existing studies, however, consider only human disassembly or robot disassembly alone. This work considers human-robot collaboration. It proposes an human-robot collaborative disassembly line balancing model considering stochastic task time, where an AND/OR graph is adopted to describe a product’s disassembly process. The objectives are to maximize the total profit and minimize energy consumption. A Pareto improved multi-objective shuffled frog leaping algorithm with a stochastic simulation strategy is proposed to solve the model. In addition, an elite strategy is introduced in global search to enhance the algorithm’s optimization capability. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of this algorithm are demonstrated. Its comparison with some most popular state-of-the-art methods is performed.Note to Practitioners—This paper is motivated by the benefits of human-robot collaboration in the disassembly systems. The presented approach is suitable for disassembly lines with multiple objectives, and the weight of each objective cannot be accurately grasped. Most of the existing operation allocation methods are based on the correlation between humans and robots and the factors affecting disassembly. This paper suggests the selection of humans and robots is completely random and decided by an optimization algorithm. This paper designs an improved multi-objective shuffled frog leaping algorithm based on Pareto’s rule. Experimental results show that this algorithm can be applied to solve practical disassembly line balancing problems.

Proper disassembly operations organization and workstation assignment can help increase the efficiency of disassembly systems that are critical for recycling and remanufacturing of end-of-life (EOL) products. A parallel disassembly system layout allows diversification of disassembly tasks and increases flexibility. In this work, a parallel disassembly balancing model considering hiring workers with government benefits (WGB) is established. To quickly find an optimal solution to the model, a salp swarm algorithm (SSA) with a new encoding and decoding process is developed. Moreover, we use the well-known mathematical optimization technique CPLEX to verify the correctness of the proposed model and use a genetic algorithm (GA), a constrained decomposition approach with grids’ optimization (CDG), and a random search (RS) algorithm to show the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can perform well on the proposed problem, which is conducive to the society accepting more WGB into the workplace.

Waste recycling is an important part of resource reuse and environmental protection. The study of disassembly lines deals with the process of recycling and remanufacturing end-of-life products. The performance of a disassembly line is affected by many factors, especially the operation cost of workstations, the precedence relationships among disassembly tasks, the skill level of workers, and their learning speed. This study considers the learning effect of disassembly workers, establish a mixed integer programming model of the disassembly balancing problem, and explores the search for optimal solution. It allocates tasks and multiskilled workers on workstations to maximize disassembly profits in the disassembly process. To solve it, an improved fruit fly optimization algorithm is proposed, and three methods are designed for the smell search. At the same time, the visual search is also designed to avoid the problem of falling into local optimum. The validity and effectiveness of the proposed algorithm are verified with experiments that compare the results with CPLEX, a well-known IBM optimizer, and some popular peer algorithms.

To investigate the impacts of monetary policies on the jump risk of Chinese stock market, we introduce them into an exponential generalized autoregressive conditional heteroskedasticity with autoregressive jump intensity (EGARCH-ARJI) model. A new jump model, i.e., the EGARCH-ARJI model with monetary policy (EGARCH-AM), is constructed. Moreover, investor sentiment is considered to investigate the interaction effect of a monetary policy and investor sentiment on the jump intensity. Results show that the announcement of an interest rate policy has significantly positive effect on it, while the effects of the announcement and implementation of a required reserve ratio policy are not significant. In addition, the interaction effect of an interest rate policy and investor sentiment on the jump intensity is positive. The interaction effect of the announcement of a required reserve ratio policy and investor sentiment is negative. The interaction effect of the implementation of a required reserve ratio policy and investor sentiment does not exist. The research results are of guiding significance for policy makers and investors to fully learn the time-varying volatility and jump risk of stock markets.

To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China and Hong Kong under different volatility regimes. Multiple time-varying copula models are used to analyze the dynamic dependences of the jump risks between the two markets. Furthermore, we construct dynamic hedging portfolios for their spot and futures markets, estimate the minimum risk hedging ratios, and measure the hedging performance. Compared with other benchmark models, the results show that the proposed one has the best fitting effect for the Chinese stock markets. The correlations between the Chinese mainland and Hong Kong markets are always positive. When constructing hedging portfolios, the proposed model is superior to other models, which means that introducing asymmetric shocks on both normal and jump volatilities into a Markovian ARJI-GARCH model can effectively improve the performance of hedging portfolios. In addition, the results of the robustness test indicates that our proposed model performs well and is robust.

Recently, energy conservation in manufacturing industry, particular in energy-intensive industries, receives much attention in order to meet the environmental protection and sustainable development needs. Optimal job scheduling is of great importance in reducing unnecessary energy consumption. To this end, both energy and time-related criteria need to be taken into consideration to achieve an efficient and sustainable production process. Generally, it is difficult to obtain the accurate processing time of jobs in advance due to various uncertainties in open shop scheduling problems arising from manufacturing and service systems. This work formulates a stochastic multiobjective hybrid open shop scheduling problem that consists of open shop and parallel-machine models. First, a multiobjective chance-constrained program is established to minimize total tardiness and energy consumption while meeting makespan requirements. Second, we newly develop a multiobjective framework integrating a brain storm optimizer and a simulation system to solve this problem. We combine population evolution to enhance exploration and external archive evolution to strengthen exploitation into the brain storm optimizer to seek for promising solutions. A simulation system is accordingly designed by using stochastic simulation and discrete-event simulation to assess the searched solutions. Finally, by conducting experiments and comparing the proposed method with several existing algorithms and an exact solver, our results confirm that it significantly outperforms its peers in tackling the considered problem.

The unmanned aerial vehicle (UAV) patrol inspection has become an efficient method to ensure the operation condition of transmission lines. The detection of key components with defects in transmission lines is a critical task in maintaining a power system’s stability. However, the complex inspection environment and the imbalance between the number of normal component samples and that of defect samples significantly affect the detection accuracy. In this article, we present a novel method for defect detection in UAV patrol images, based on a hierarchical convolutional vision transformer (HC-ViT) and a simple contrastive masked autoencoder (SC-MAE). The HC-ViT backbone integrates the advantages of vision transformer and convolution, while the SC-MAE is a self-supervised learning method that extracts useful features from normal samples. By introducing the normal features into the backbone, we enhance the performance of the defect detection task. We demonstrate the effectiveness of our method through experiments, and show that it can leverage a large amount of unlabeled normal images, reducing the need for manual annotation. Our method offers a new way to exploit the potential features of patrol inspection images.

The process of recycling and remanufacturing begins with disassembly. Through disassembly, the components with recycling value are decomposed. However, with the rapid development of production automation, designers often ignore the fact that manual operation is flexible but fails to achieve maximum production efficiency and profit. Therefore, the consideration of human factors in disassembly lines holds significant importance. This study delves into the multi-objective optimization of a U-shaped disassembly line balancing problem involving multiple products. A comprehensive objective function is developed, taking into account various factors including employee fatigue and other factors. To address the aforementioned problem, this study uses a collaborative resource allocation strategy within a multi-objective evolutionary algorithm based on decomposition. By comparing the results of different experimental cases, this paper shows that the proposed algorithm is more competitive than the carnivorous plant algorithm, fruit fly optimization algorithm, and Pareto archiving evolutionary strategy.

Autonomous driving has been successfully realized in particular areas such as logistics distribution centers, container terminals, and university campuses. Robotaxi could be another potential application in the near future. This work studies a robotaxi dispatch problem during the pandemic time. It proposes a multi-objective optimization model to minimize the number, waiting time, and driving distance of the robotaxis. Besides, a dispatch strategy is innovatively designed according to a defined severity degree of the pandemic. A virus infection rate can be decreased by reducing contact among passengers. A two-stage nondominated sorting genetic algorithm (NSGA-TS) is proposed to solve the problem. Three operations are used to generate offspring solutions, which can ensure the diversity of the population and speed up the convergence of the algorithm. The effectiveness of NSGA-TS is verified compared with two popular multi-objective optimization algorithms, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D) and nondominated sorting genetic algorithm II (NSGA-II). Experimental results show that the proposed model performs well on the studied problem. It can reduce the virus infection rate by decreasing contact among passengers at different risk levels of the pandemic while accomplishing passenger orders. This work is conducive to society building intelligent transportation in the post-pandemic era.

The adjustment effect of the anchoring effect and online reviews on consumer cognition has grown to be a significant element influencing business pricing. This study explores the effects of online reviews and price anchoring on company pricing and profits by building an online product pricing model based on expected utility theory from the perspective of consumer purchasing psychology. The findings show that firms must take consumer anchoring psychology into account when making decisions if they want to increase revenue. Different pricing strategies are used depending on the variables associated with the quality of online reviews. The higher the sensitivity coefficient of reviews, in particular when the quality of the reviews is higher than a specified value, the bigger the profit. In the anchor point, the optimal price is rising. When a company chooses a higher price policy, the optimal price steadily decreases with the degree of anchoring.

Price is an important index of consumers' purchase choice, and the price comparison behavior of consumers in the decision-making process also affects the profit and loss of their own purchase utility to varying degrees. Based on multinomial logit(MNL) model, the reference price is incorporated into product line development and design, and the pricing decision of product line and value-added services bundle is studied. The influence mechanism of reference price effect on optimal product pricing and maximum profit is analyzed, and the deviation of strategic decision-making caused by not considering reference price effect is discussed. The results show that the reference price effect has a positive impact on the lowest price products in the product line, but has a negative impact on the high price products in the product line, the total market share of the firm and the total profit. When the reference price effect is ignored, the pricing of different quality products and services in the product line will be higher or lower, and the total market share and total profit will be higher. The results can provide theoretical support for product line design and pricing decision.

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions.

Emergency vehicles (EVs) are crucial in responding to time-critical events such as traffic accidents, medical emergencies, and fires in urban areas. Most traffic control approaches try to reduce the travel time of EVs by giving them the highest road-use priority, which may cause delays for other nearby traffic participants and reduce the smoothness of normal traffic. This work proposes a novel approach to reduce both the travel time of an EV and the negative impact on normal traffic by dynamically evacuating traffic adjacent to an emergency path. The approach periodically acquires a subnet for each road segment of the emergency path based on dynamic traffic conditions. Regular vehicles on the subnet are restricted from using the emergency path, which minimizes the time for emergency service delivery. The experimental results show that the proposed approach outperforms the existing approach in many metrics, such as the travel time of the EV and the additional delay of normal traffic. In addition, this work performs sensitivity analysis on regular vehicles' compliance rate to evacuation. The experimental results show the superiority of the proposed approach at different compliance rates.

Un-signalized occluded intersections are residential road intersections with narrow lanes and surrounding buildings, which are prone to traffic accidents. This work uses deep reinforcement learning to design driving strategies for Autonomous Vehicles (AVs) for avoiding collision and reducing damage to electric bicycles (e-bikes) with dangerous behaviors at un-signalized occluded intersections. The conflict-avoidance behavior of e-bikes is modeled. It adopts a multi-objective reward function that considers the injury severity of e-bike riders and the driving safety and comfort of AVs. A deep deterministic policy gradient method is used to train the model to control the acceleration and steering of AVs. The performance of the proposed method is compared with that of an autonomous emergency braking system and a risk-aware high-level decision strategy by simulation experiments. Experimental results show that the driving strategy can reduce the collision probability by 26.38% on average, and the injury can be reduced by 14.05% on average when the collision is unavoidable. To our knowledge, this is the first paper that employs reinforcement learning to model and design driving strategies for AVs conflicting with e-bikes. It can be used to improve the state of the art in AV control and safety at intersections.

The progress of science and technology speeds up the replacement of products and produces a large number of end-of-life products. Traditional incineration causes a waste of resources and pollution to the environment. Disassembling and recycling end-of-life products are the recommended way to maximize the utilization of resources and reduce environmental pollution. Disassembly performance is affected by many factors, such as the disassembly posture of the human body, the fatigue of workers on a workstation, disassembly profit, and task precedence relationship. In this article, a mixed integer linear programming mathematical model for U-shaped layout disassembly line balancing problems is developed, in which the balance of workers’ fatigue indices is an optimization objective in addition to disassembly profits. An efficient solution to the problem that uses a collaborative resource allocation strategy of the multiobjective evolutionary algorithm is proposed. The linear programming solver CPLEX is used to verify the accuracy of the model and compared with the proposed algorithm. Experiments demonstrate that the algorithm is significantly superior to the CPLEX solver in handling large-scale cases. The proposed algorithm is also compared with two well-known algorithms, which further verifies its superiority.

From the aspect of behavioral finance, which is an emerging area integrating human behavior into finance, this work studies a robust portfolio problem for loss-averse investors under distribution and mean return ambiguity. A loss-aversion distributionally-robust optimization model is constructed if the return distribution of risky assets is unknown. Then, under the premise that the mean returns of risky assets belong to an ellipsoidal uncertainty set, a model under joint ambiguity in distribution and mean returns is constructed. This study solves both robust models and derives their analytical solutions, respectively. Moreover, the effect of ambiguity aversion and loss aversion on robust optimal portfolio returns is studied. The results show that ambiguity-neutral investors who do not know the return distribution obtain more robust optimal portfolio returns than ambiguity-averse investors who are unaware of both the distribution and mean return. The difference between them decreases with the increase of loss aversion coefficients and increases with ambiguity aversion coefficients. Both loss aversion and ambiguity aversion play important roles in investors’ behavioral portfolio selection.

With the development of IoT, virtual reality, cloud computing, and digital twin technologies, the advent of metaverse has attracted increasing world attention. Metaverse integrates and applies multiple emerging technologies to cloud education, smart health, digital government, and emergency evacuation. Evacuation systems are of great importance to ensure life safety. Due to panic, people in a building may not be able to make the right judgment to choose an optimal path to leave the building in case of an emergency event such as a fire. As a branch of machine learning, deep reinforcement learning (DRL) can model an evacuation scene, collect real-time information, such as crowd distribution and disaster location, find the optimal escape path with a path-planning algorithm, induce the movement state of the crowd through dynamic guidance signs, and improve the evacuation efficiency. In this article, we apply DRL technology to solve the efficient emergency evacuation problem with the help of metaverse and show a training system built upon metaverse that would enable evacuees to choose the most efficient route and leave the building in the least amount of time. The information collected by various sensors, such as video cameras and smoke detectors, can give a whole picture of the status of the building in a real-time manner. The collected data are processed by cloud servers in which a DRL model is trained to dynamically guide evacuees. Experiments in different simulation scenes demonstrate that the proposed method is superior to the traditional static guidance method in saving evacuation time. It can effectively avoid major crowding along the evacuation route and improve evacuation efficiency.

Emergency vehicles (EVs), such as ambulances, police vehicles, and fire-fighting trucks, play an essential role in delivering emergency services in our society. To decrease the negative impact of EVs on normal traffic, a traffic guidance strategy is proposed for the evacuation of regular vehicles on the road of the EV. Queuing theory is used to provide equilibrium guidance for the evacuation. Furthermore, lane-changing and traffic light preemption strategies are used to prioritize the EV. A simulation experiment is conducted on a map of Huangdao District, Qingdao City, China with the platform of SUMO. The proposed method is validated on three types of traffic flow density. Compared with the existing state-of-the-art strategies, the superiority of our approach is verified from the aspects of EV’s average waiting time and the time loss of other vehicles.

Disassembly planning and sequencing play an important role in recycling a fast-growing number of end-of-life products. Optimal sequences can effectively reduce carbon emissions and save natural resources in the remanufacturing industry. Considering the development of intelligent manufacturing technology, this work deals with the optimization problem of selective disassembly sequences with an objective of maximizing disassembly profit. Disassembly sequences are generated based on AND/OR graphs. After setting up an environment matrix based on such graphs, this proposes a Q-learning technique to find an selective optimal disassembly sequence. The algorithm is applied to real-life disassembly cases. Experimental results show that the algorithm is superior a popularly-used genetic algorithm (GA) in both computing speed and solution quality through their various comparisons.

With more and more waste products are discarded, how to recycle them has become an urgent issue. Disassembling these discarded products is a critical step to take. With disassembly, we can maximize resource utilization and greatly save manufacturing costs. There are many influencing factors in a disassembly process. In this paper we consider the impact of disassembly tools deterioration rate on disassembly time and establish a mathematical model to minimize the disassembly time. We use the advantage actor-critic algorithm in reinforcement learning to solve this model. The correctness and superiority of the algorithm are verified by comparing with the actor-critic algorithm.

Non-negative latent factor analysis models such as tensor factorization have achieved significant success in collaborative-filtering-based recommendation tasks because they can perform representation learning to high-dimensional and incomplete data efficiently. However, they also suffer from either slow computational speed or representation accuracy loss. To address these issues, this paper presents a Partition-based Localized Tensor Factorization (PLTF) approach for predicting the missing values in the user-item-time rating tensors. First, a large sparse tensor is constructed to model users’ rating behavior. Then, it is transformed into recursive bordered-block-diagonal form by using the graph partitioning technology. Smaller and denser sub-tensors are extracted and factorized by using CP decomposition algorithm. Experimental results on sparse tensors from real applications show the efficiency of the proposed PLTF approach.

With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, this paper focuses on human-robot collaborative disassembly lines. Specifically, this work proposes a multi-product human-robot collaborative disassembly line balancing model to tackle the inflexibility of single product disassembly and inconsistency in recovery values of different product components. Its objective is to maximize disassembly profit. As a commercially available solver, IBM’s CPLEX is used to obtain the exact solution of DLBP and verify the correctness of the proposed mathematical model. A discrete shuffled frog leading algorithm is newly designed to solve the sizable problems. Experimental results show that the proposed algorithm has a fast convergence rate and can find solutions consistent with those with CPLEX but requires much less time than the latter, thus advancing the field of disassembly automation.

Designing a disassembly line layout is an important part of the recycling process of end-of-life products. Parallel disassembly lines have the characteristics of high disassembly efficiency and can disassemble multiple different products simultaneously. This work formulates a mathematical model for optimizing such lines in terms of disassembly profit and the number of skills. It also proposes an improved bat algorithm based on the Pareto principle to solve the model. In order to verify the effectiveness and feasibility of the proposed algorithm, it is compared with the non-dominated sorting genetic algorithm and a decomposition-based multi-objective evolutionary algorithm. Experimental results indicate that this algorithm has outstanding solution capability and is thus suitable for solving parallel disassembly line balance problems.

Proper disassembly operation can help increase the recovery of industrial valuable supplies and end-of-life products. To solve a disassembly line balancing problem, this work focuses on a parallel layout and proposes an intelligent optimization method to maximize disassembly profits. It first formulates a parallel multi-product disassembly line balancing problem model by taking disabled workers into account. It then designs a salp swarm algorithm with innovative encoding and decoding processes. This work finally compares the proposed algorithm with a generic algorithm. Experimental results show that the newly proposed model and algorithm can well deal with the presented problem.

Vehicle Ad-hoc Networks have laid an essential technical foundation for realizing intelligent transportation. Unexpected mobility change of a specific node often causes a communication link failure. Thus, this work proposes a node backup strategy for routing protocol in software-defined vehicular networks. The core of the strategy is to promote communication link stability through backup nodes. A node backup routing algorithm is designed to search for alternative nodes for each node in a communication link. A node can flexibly select the next-hop node during packet transmission based on actual conditions. When an unexpected mobility change of a specific node causes a communication link failure, we can restore the link by enabling the alternative nodes. The influence of various factors on packet reception rate and communication delay is studied through simulation experiments. By comparing with two existing routing protocols, the effectiveness of the proposed approach is verified.

Under platform economy, prices, reviews, and sales are the three most concerned purchase decision factors for consumers. However, different customers have different sensitivity to the same decision factors. Therefore, it would be an important to study the sensitivity of the consumers to reviews, price, and sales. Based on the Multinominal Logit Model (MNL Model), this paper constructs a two-stage pricing model for new products of platform enterprises, and studies the influence of price, review and sales on enterprise profit. Then it analyzes the influence mechanism of consumers’ sensitivity to price change, product cost and consumers’ valuation of product quality on product pricing and enterprise profit. After that, it further formulates the two-stage optimal pricing strategy for product sales. Research shows that enterprises should not only consider consumers’ sensitivity to comments, price and sales volume, but also learn from the previous sales experience when making pricing strategy. The research results can provide theoretical basis and decision support for product pricing and operation management of platform enterprises.

If people simply trash their used products, they would face many issues such as pollution to environment and resource waste. Recycling and remanufacturing used products are thus necessary, which makes the study of disassembly line balancing problems important. At present, manual disassembly is popular and it does not guarantee personal safety in the event of dangerous disassembly parts. Targeting at this problem, a mixed human-robot disassembly method is proposed. An improved Q-learning algorithm based on reinforcement learning is used to solve the two-sided disassembly line balancing problem with the objective of minimizing total disassembly time. The improved algorithm is compared with the SARSA algorithm. The results show that it can find better solutions than SARSA, and outperforms SARSA particularly in large-scale cases.

In the freemium business model, how to price value-added services and design effective strategy to achieve the sustainability of value-added services promotion is of great significance to enterprises. By constructing a monopolistic freemium enterprise, this paper uses a Multinational Logit model (MNL model) to analyze value-added services pricing and two kinds of value-added services promotion strategies (the quality reduction strategy of basic product and the price discount strategy of value-added services) with heterogeneous sticky-users demand, and then discusses the optimal promotion strategy. The results show that: Both the quality reduction strategy of basic product and the price discount strategy of value-added services can have positive impacts on the profit of enterprise. The sticky users demand valuation plays a positive role in promoting the optimal profit of enterprise. The optimal promotion strategy is the price discount strategy of value-added services. This study can provide a theoretical basis and decision support for the operation and management of the freemium enterprises.

With the rapid development and upgrade of electronics and related technologies, more and more discarded and end-of-life products are generated and must be properly handled and recycled. Disassembly lines are a key to their efficient recycling process. A parallel disassembly line offers high profit, low energy consumption, and high efficiency. In this paper, a linear programming model for optimal human-robot collaborative disassembly is established. The goal is to maximize disassembly profit. An improved Moth-Flame optimizer (MFO) is proposed and the crossover part of the algorithm is improved based on this problem’s characteristics. Experiments with practical cases involving multiple products of disassembly are used to test the model and algorithm. The result shows that MFO has obvious advantages over a commonly-used algorithm in solving parallel disassembly line balancing problems.

With the advancement of society and technology, consumers are becoming more personalized and more willing to buy new products. To meet the diverse needs of consumers, the design and development of product lines have become an important strategic issue of enterprises. Based on the consumer choice model, this paper aims at the design and development of product lines and the purchase behavior of consumers. A two-stage pricing model is constructed under the condition of bundled sales of products and services. This paper analyzes the impact that enterprises should consider products with value-added services and consumers’ purchasing behavior on product line two-stage pricing. Research shows that the level of product value-added services and the degree of enterprise strategy will have an impact on the price of the product line and the enterprise’s profit. When the service level is higher, the enterprise’s product line price and profit will increase, and when the enterprise discount higher, the enterprise’s total profit and product line price will decrease.

The quality of durable consumer goods is more and more concerned by consumers, and the development of value-added services to improve product quality has become an important way for enterprises to obtain profits. Based on the multinomial logit (MNL) model, this paper establishes a product line optimization model considering value-added services, which helps find the optimal pricing, market share and maximum profit. Through numerical experiments, the effects of the ratio of service price to product price, product quality, service quality, utility loss caused by product failure on the optimal solutions are studied. The study finds that when developing a product line, increasing the relative price of services while reducing product pricing is the optimal strategy. The research results can provide theoretical basis and decision support for the pricing of durable consumer goods and value-added services.

With the development of science and technology, a large number of electronic products have been discarded and become waste products. To obtain economic benefits and protect the environment, disassembly lines are designed to disassemble valuable parts from waste products. This paper proposes a mathematical model for the human-robot collaborative multiproduct hybrid disassembly line balancing problem with the disassembly revenue being the objective. A hybrid line combines a single-row line and a U-shaped line. We use the multi-neighborhood parallel greedy search algorithm to solve the model. Based on the algorithm, an alternate neighborhood search scheme consisting of different actions is designed. Some real-world cases are used to examine the feasibility of the proposed algorithm. The experimental results show that the multi-neighborhood parallel greedy search algorithm can solve the multi-product hybrid disassembly line balancing problem effectively.

In the Internet era, the continuous innovation and progress of science and technology promote the renewal of electronic and electrical products and tend to shorten their life cycle. As the recycling rate of these waste products is very low, this causes a great waste of resources. How to disassemble and recycle valuable parts is a common problem faced by the world. In essence, the recycling of waste products by enterprises is to obtain most valuable parts and components from obsolete products to gain profits. This paper considers the traditional linear disassembly line, which is widely used in factories at present. By combining the Brainstorming optimization (BO) algorithm with the K-means clustering algorithm, this work proposes a novel Improved Brainstorming optimization algorithm to obtain the near optimal solution quickly. It is compared with an Artificial Bee Colony algorithm and Gray Wolf optimization algorithm to verify its superiority in solving disassembly line balancing problems.

With the rapid development of science and technology, a large amount of electronic waste is inevitably generated from various discarded and End-Of-Life electronic products. If these products are not handled properly, they can cause environmental pollution as well as loss of resources. As an important part of remanufacturing, disassembly is usually done manually with low efficiency and high labor cost. In this paper, parallel disassembly lines with multiple robots are proposed. These robots can run automatically and be used to perform disassembly in an optimal disassembly mode. A multitype robot can be flexibly set with multiple functions. A mathematical model is established to assign disassembly tasks to the robots such that a line can achieve the maximum profit and minimum carbon emissions. An improved multi-objective multi-verse optimizer is proposed and applied to a set of instances. Experimental results show that the algorithm has an overwhelming performance advantage over the other three commonly-used algorithms in solving this problem. It has better performance than the other peer algorithms in solving parallel disassembly line balancing problems.

The remanufacturing, recycling, and reusing of waste products are particularly important to solve the problem of the resource shortage. Disassembly is a key step in the recycling process. How to minimize the negative impact of greenhouse gases on the environment has attracted extensive attention. This paper studies the disassembly line balancing problem to minimize the carbon emissions generated in the disassembly process. A Q-learning algorithm in reinforcement learning is applied to solve the disassembly line balancing problem. Through the analysis and comparison with the state-action-reward-state’-action algorithm to address the same real-life cases, it is proved that the Q-learning algorithm has good performance in most cases. In terms of solution speed, the proposed method is faster in both small-scale and large-scale cases.

Nowadays, the recycling of waste products has attracted extensive attention in academia and industry. In the layout design of disassembly lines, single-row and U-shaped hybrid disassembly lines have different application scenarios. Considering workstation resource configuration, disassembly line cycle time, and disassembly task precedence relationship, we address a Multi-product Hybrid-disassembly-line-balancing Problem (MHP), and establish its mathematical model with the objective of disassembly profit maximization. In addition, the union variable neighborhood descent (U-VND) algorithm is used to solve the problem, in which two kinds of neighborhood structures composed of different actions is designed. Experimental results and comparative analysis show that the proposed algorithm can quickly obtain stable and high-quality solutions, which verifies the validity of the neighborhood structure and the correctness of the model.

With the rapid development of Internet of Things (IoT), much data has been produced, and new requirements have been posed for data mining. Clustering plays an essential role in discovering the underlying patterns of IoT data. It is widely used in health prognoses, pattern recognition, information retrieval, and computer vision. Density clustering is crucial to find arbitrary-shaped clusters and noise points without knowing the number of clusters in advance. However, its efficiency and applicability are reduced sharply when there exists mutual interference among parameters. In this article, a new algorithm called density-center-based automatic clustering (DAC) is proposed. First, this work presents a nonparametric density computing method. Second, it proposes to use an adaptive neighborhood whose radius is automatically calculated based on all the points in a data set. Finally, it selects appropriate density centers from a decision graph, which merge their surrounding points into the same groups. Experiments are conducted to show that DAC has higher accuracy than six classic and updated algorithms. Its effectiveness is shown via data from photovoltaic power and oil extraction systems. As an outstanding feature that its compared peers lack, it can determine parameters automatically. Thus this work greatly advances the state-of-the-art of clustering algorithms in the field of IoT data analysis.

Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy. Disassembly sequencing and line balancing problems are indispensable to recycling and remanufacturing EOL products. A set of subassemblies can be obtained by disassembling an EOL product. In practice, there are many different types of EOL products that can be disassembled on a disassembly line, and a high-level uncertainty exists in the disassembly process of those EOL products. Hence, this paper proposes a stochastic multi-product multi-objective disassembly-sequencing-line-balancing problem aiming at maximizing disassembly profit and minimizing energy consumption and carbon emission. A simulated annealing and multi-objective discrete grey wolf optimizer with a stochastic simulation approach is proposed. Furthermore, real cases are used to examine the efficiency and feasibility of the proposed algorithm. Comparisons with multi-objective discrete grey wolf optimization, non-dominated sorting genetic algorithm II, Multi-population multi-objective evolutionary algorithm, and multi-objective evolutionary algorithm demonstrate the superiority of the proposed approach.Note to Practitioners—Disassembly line balancing has been widely recognized as the most ecological way of retrieving EOL products. Through in-depth research, we present a Stochastic Multi-product Multi-objective Disassembly-sequencing-line-balancing Problem. Furthermore, we consider that the uncertainty of products might cause disassembly failure. To solve this problem effectively and quickly, we combine the simulated annealing algorithm with the Grey Wolf Optimizer. The results show that the algorithm can effectively solve the proposed problem. The disassembly scheme provided by the obtained solution set offers a variety of options for decision-makers.

Wire rod and bar rolling is an important batch production process in steel production systems. A scheduling problem originated from this process is studied in this work by considering the constraints on sequence-dependent family setup time and release time. For each serial batch to be scheduled, it contains several jobs and the number of late jobs within it varies with its start time. First, we model a rolling process using a Petri net (PN), where a so-called rolling transition describes a rolling operation of a batch. The objective of the concerned problem is to determine a firing sequence of all rolling transitions such that the total number of late jobs is minimal. Next, a mixed-integer linear program is formulated based on the PN model. Due to the NP-hardness of the concerned problem, iterated greedy algorithm (IGA)-based methods by using different neighborhood structures and integrating a variable neighborhood descent method are developed to obtain its near-optimal solutions. To test the accuracy, speed, and stability of the proposed algorithms, we compare their solutions of different-size instances with those of CPLEX (a commercial software) and four heuristic peers. The results indicate that the proposed algorithms outperform their peers and have great potential to be applied to industrial production process scheduling.Note to Practitioners—This work deals with a scheduling problem of a batch production process, i.e., wire rod and bar rolling, which is modeled by a Petri net (PN). Due to the NP-hardness of the concerned problem, four iterated greedy algorithm-based methods are developed to solve it. The proposed methods are validated and tested by comparing their solutions with those of four heuristic peers and the exact ones (when available via CPLEX). Extensive experimental results show that they can fast solve one-week-scale instances with better performance than their peers’, thereby proving the readiness to put them in industrial use. When solving a one-month-scale instance, the proposed methods show much better performance than others.

Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products’ exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance.

As one of the crucial tasks of natural language processing, machine reading comprehension has gained increased attention in recent years. In this paper, we propose a four-way bidirectional attention network for a multiple-choice reading comprehension task, where every question comes with a set of candidate options and only one correct answer. Current methods on such tasks usually judge options independently and ignore their relations. Thus, this work designs a four-way bidirectional attention strategy to formulate the interactions among the passage, questions and candidate options. In particular, the relations among options are well represented. This enables the model to leverage the option correlation information for inferring the final answer accurately. The experimental evaluations on the CosmosQA dataset demonstrate the competitive performance of our model, and confirm the effectiveness of the option comparison strategy.

To meet the needs of the Internet of Things, every edge device is equipped with the functions of data collection, analysis, calculation, communication, and intelligence. Based on the consumption pattern of offline experience and online purchase, and considering the impact of product quality differences, product defects, and offline service level on customers' purchasing behavior, this paper uses the model (Multinominal Logit Model) to research customers' choice behavior and online product pricing. This paper takes the pricing of dual-channel retailers in different channels as the background, and how to maximize the retailer's profit as the goal, establishes the loss cost model of customer returns, and analyzes the influence of quality problem returns on the optimal pricing and profit of retailers in different channels. The study found that the offline service level remains at 0.24 and retailers can obtain the best profit; the optimal price decreases with the online product quality and the optimal profit increases. In the omni-channel environment, customers can buy products according to their own utility and preferences freely switch between various channels, retailers in the face of customer return this situation, can start from their own interests, provide appropriate service level, reasonable control product quality, make the optimal pricing, maximize their own profits. This study expands the theory of online product pricing from the perspective of customers behavior, provides a more flexible pricing mechanism for enterprises, and speeds up the development and application of intelligent edge computing systems.

A reasonable disassembly line structure and layout are particularly important in advancing disassembly technology. In this work, destructive and non-destructive disassembly modes are considered in multiple-product partial U-shaped disassembly-line-balancing. A mathematical model is established to maximize disassembly profit and minimize disassembly energy consumption for a U-shaped disassembly line. A multi-objective discrete chemical reaction optimization algorithm is then proposed to solve it. A crowded distance mechanism and elitist strategy are designed to obtain non-dominated solutions to accelerate its the convergence speed. The established model and proposed algorithm are applied in a ballpoint pen and radio set cases, and its superiority on a U-shaped disassembly line is verified by comparing it with two commonly used optimization methods.

Disassembly Line Balancing Problems have received much attention from practitioners and researchers due to their importance in sustainable economic development. This work focuses on a U-shaped disassembly line balancing problem and establishes its mathematical model by considering multiple optimization objectives, disassembly task priority relationship, staff training cost, and the cycle time of disassembly workstations. Considering the characteristics of the problem, it proposes a collaborative resource allocation strategy for a multi-objective evolutionary algorithm based on decomposition, resulting a new method called MOEA/D-CRA for short. It allocates corresponding computing resources according to the importance of each subproblem. Four cases are used to compare the MOEA/D-CRA with two well-known algorithms. Experimental results prove that it is significantly better than its two peers.

Most successful fittings detectors are anchor-based, which is challenging to meet the lightweight and real-time requirements of the edge computing system. We propose a high-resolution real-time network HRM-CenterNet. Firstly, the lightweight MobileNetV3 is used to extract multi-level features from images. Then, to improve the resolution of the feature maps and reduce the spatial semantic information loss during the image downsampling process, a high-resolution feature fusion network based on iterative aggregation is introduced. Finally, we conduct experiments on the PASCAL VOC dataset and fittings dataset. The results show that HRM-CenterNet improves accuracy as well as robustness, and meets the performance requirements of real-time edge detection.

A style-based generative adversarial network (StyleGAN2) yields remarkable results in image-to-latent embedding. This work proposes a Detached Dual-channel Domain Encoder as an effective and robust method to embed an image to a latent code, i.e., GAN inversion. It infers a latent code from two aspects: a) a detached dual-channel design to support faithful image reconstruction; and b) a local skip connection that allows conveying pieces of information with image details. We further introduce a hierarchical progressive training strategy that allows the proposed encoder to separately capture different semantic features. The qualitative and quantitative experimental results show that the well-trained encoder can embed an image into a latent code in StyleGAN2 latent space with less time than its peers while preserving facial identity and image details well.

Industrial products' reuse, recovery, and recycling are very important due to the exhaustion of ecological resources. Effective product disassembly planning methods can improve the recovery efficiency and reduce harmful impact on the environment. However, the existing approaches pay little attention to disassembly resources, such as tools and operators that can significantly influence the optimal disassembly sequences. This article considers a multiobjective resource-constrained disassembly optimization problem modeled with timed Petri nets such that energy consumption is minimized, while disassembly profit is maximized. Since its solution complexity has exponential growth with the number of components in a product, a multiobjective genetic algorithm based on an external archive is used to solve it. Its effectiveness is verified by comparing it with nondominated sorting genetic algorithm II and a collaborative resource allocation strategy for a multiobjective evolutionary algorithm based on decomposition. Note to Practitioners-This article establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. In an actual disassembly process, a decision-maker may want to minimize energy consumption and maximize disassembly profit. This article considers both objectives and proposes a multiobjective genetic algorithm based on an external archive to solve optimal disassembly problems. The experimental results show that the proposed approach can solve them effectively. The obtained solutions give decision-makers multiple choices to select the right disassembly process when an actual product is disassembled.

In a steel industry, a slab yard plays a role of a buffer between continuous casting stage and rolling mill. An effective assignment and scheduling of cranes can guarantee the operation efficiency in the slab yard. This work studies a multicrane scheduling problem with noncrossing constraints of slabs. A mixed-integer programming model is used to formulate the problem that minimizes the whole traveling distance of all the cranes and ensures the workload balance among cranes. As it is an NP-hard problem, classical programming mathematical methods are difficult to get an optimal solution for large-size instances. Thus, we develop a branch and price algorithm to solve this problem. First, we formulate the model as a generalized set covering problem and a set partition problem. Then, we solve them and combine the solutions to obtain the solution of the original problem. Finally, we conduct computational experiments based on real data from an iron–steel plant. The comparisons of proposed methods with an exact solution method show its effectiveness.Note to Practitioners—This work deals with a multicrane scheduling problem. Aiming to minimize the total traveling distance of all the cranes, it establishes a mixed-integer programming model with a workload balance constraint on cranes. It presents a branch and price algorithm to solve the problem whose solution complexity grows exponentially with problem size. The integration of crane assignment and scheduling enables the better utilization of cranes and faster service in iron–steel enterprises and, hence, improving customer satisfaction. The experimental results reveal the effectiveness of the proposed approach. It can readily be put into use in the steel industry.

A style-based architecture (StyleGAN2) yields outstanding results in data-driven unconditional generative image modeling. This work proposes a Domain-guided Noise-optimization-based Inversion (DNI) method to perform facial image manipulation. It works based on an inverse code that includes: 1) a novel domain-guided encoder called Image2latent to project the image to StyleGAN2 latent space, which can reconstruct an input image with high-quality and maintain its semantic meaning well; 2) a noise optimization mechanism in which a set of noise vectors are used to capture the high-frequency details such as image edges, further improving image reconstruction quality; and 3) a mask for seamless image fusion and local style migration. We further propose a novel semantic alignment evaluation pipeline. It evaluates the semantic alignment with an inverse code by using different attribute boundaries. Extensive qualitative and quantitative comparisons show that DNI can capture rich semantic information and achieve a satisfactory image reconstruction. It can realize a variety of facial image manipulation tasks and outperform state of the art.

In order to better meet the needs of the green industrial Internet of things, this paper studies the anchoring psychology of online consumers and proposes a pricing model that considers online consumers to be anchored by the product price. In this model, the cognitive bias of online consumers affected by anchoring effect is described in a utility function by using the anchoring-adjustment heuristic, and the consumer choice behavior and online product pricing strategy are studied with a Multinomial Logit Model (MNL). We study the effect of price anchoring point and anchoring degree on optimal pricing. In particular, when the cost is greater than the price anchoring point, the greater the anchoring degree is, the smaller the profit will be. This paper provides a more flexible pricing mechanism for enterprises based on historical consumption data through customer behavior analysis, and it will accelerate the construction of the green industrial Internet of things.

Timely and proper recycling of end-of-life products promotes the sustainable development of our human society. Cost-effective and energy-efficient disassembly is a crucial step to realize such recycling. A disassembly process is usually uncertain because of the different quality of subassemblies in an end-of-life product. This work considers resource constraints and operation failure in such a process. A novel mathematical model is formulated with the objectives to maximize profit, minimize energy consumption, and minimize the total need for disassembly resources. A new solution method called a multi-objective discrete migratory bird optimizer is proposed to solve it. A real-life case is given to show its effectiveness and feasibility. Experimental results show that the superiority of the proposed algorithm over a well-known nondominated sorting genetic algorithm II and a multi-objective grey wolf optimizer.

A Disassembly Line Balancing Problem (DLBP) exists in the remanufacturing of discarded products. It involves such factors as sequence-dependent among components, multi-resource constraints, limited number of workstations, uncertainty of disassembly time, and disassembly failure risk. Effective decisions can be made by taking them into full consideration. This work establishes a stochastic sequence-dependent multi-objective DLBP model subject to disassembly failure and resource constraints. Its objectives are maximization of profit and minimization of energy consumption. A multi-objective cuckoo search algorithm is proposed. Then, three real products are disassembled to verify the effectiveness and feasibility of the proposed approach. Experimental results show the superior of the proposed algorithm over multi-objective artificial bee colony algorithm and non-dominated sorting genetic Algorithm II.

A disassembly line balancing problem (DLBP) exists in the recycling process of end-of-life (EOL) products. It involves such factors as uncertainty of disassembly time and disassembly failure risk. Effective decisions can be made by taking them into full consideration. Under the constraints of disassembly precedence relationships and cycle time, this work establishes a stochastic multi-objective DLBP model subject to disassembly failure based on a disassembly AND/OR graph of EOL products. It considers disassembly failure risk and comprehensively evaluates the profit, energy consumption, average idle time of workstations, and hazard disassembly. Then, a new multi-objective discrete brainstorming optimizer that combines stochastic simulation is proposed for obtaining high- quality feasible solutions. Experimental results show the validity of the proposed algorithm. It outperforms both nondominated sorting genetic algorithm II and multi-objective discrete grey wolf optimizer.

Reliable detection of obstacles around the vehicle is crucial for autonomous cars. We present a novel and robust ground shape invariant feature method for general obstacle detection with a car-mounted monocular fisheye camera. Both stationary and moving obstacles can be detected by our approach without recovering the camera motion. Firstly, In order to compute the ground shape invariant feature, the image is mapped into the top view image. And then feature points are extracted and matched between adjacent frames. Secondly, the points are grouped according to image patch partition. Finally, the ground shape invariant feature is computed for each group of points to detect obstacle points. Extensive experiments have been carried out with prerecorded video sequences including various obstacle types, various scenes and various illumination conditions. The experimental results show promising detection performance of the proposed method.

There is a growing concern in recycling plants for minimizing the negative environmental impacts (such as carbon emissions) of disassembling end-of-life products. Uncertainty caused by their different usage stages exists when disassembling them. In this paper, we propose a stochastic multi-objective disassembly sequencing and line balancing problem based on an AND/OR graph. By considering disassembly failure risk, we construct objectives of maximizing profit and minimizing carbon emission and energy consumption to help sustain economic development. Then, we propose a novel multi-objective discrete grey wolf optimizer to solve it. We show its effectiveness via a product example. The results show the superiority of the proposed algorithm over classical non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition.

This article studies a steelmaking-continuous casting (SCC) scheduling problem by considering ladle allocation. It takes technological rules in steel manufacturing and ladle-related constraints into account. A scheduling problem is formulated to determine allocation equipment for jobs, production sequence for jobs processed by the same equipment, and modification operations for empty ladles after their service for jobs. To ensure the fastest production and least energy consumption, we present a mixed integer mathematical programming model with the objectives to minimize the maximum completion time, idle time penalties, and energy consumption penalties related to waiting time. To solve it, we develop a two-stage approach based on a combination of scatter search (SS) and mixed integer programming (MIP). The first stage applies an SS algorithm to determine the assignment and sequence variables for charges. For the obtained solution, we construct a temporal constraint network and establish an MIP model at the second stage. We apply ILOG.CPLEX to solve the model and find the final solution. We analyze and compare the performance of the proposed approach with a hybrid method that combines a genetic algorithm with MIP on instances constructed from a real iron-steel plant. To further verify the effectiveness of the proposed algorithm, we compare its results with optimal solutions of the constraint-relaxed original problem. The experimental results show the effectiveness of the proposed approach in solving the SCC-scheduling problem.

Considering time-varying transition probability (TVTP), this article combines Markov regime switching with a dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model to construct a new hedging model and study a state-dependent minimum variance hedging ratio. A two-stage maximum likelihood method is constructed to estimate the model parameters. A filtering algorithm is used in an estimation process. Empirical results on commodity futures hedging show that compared with other benchmark models, the proposed one has the best fitting effect. In addition, in terms of hedging effectiveness, the proposed model is superior to other models in most cases, which means that introducing TVTP into a DCC-GARCH model can effectively improve the performance of hedging portfolio. Note to Practitioners-This article deals with a state-dependent minimum variance hedging problem. It combines a time-varying Markov regime switching with dynamic conditional correlation generalized autoregressive conditional heteroscedasticity named DCC-GARCH to construct a new hedging model and estimates a state-dependent hedging ratio. Empirical results from commodity futures hedging show that introducing TVTP into the DCC-GARCH model can effectively reduce portfolio risk and provide better hedging performance than other traditional models, including Markov regime switching DCC-GARCH with a fixed transition probability, DCC-GARCH, ordinary least squares, naïve hedging strategies, and unhedged spots. Thus, this article is of guiding significance for hedgers to fully learn the hedging rules of futures market and avoid the spots price risk.

Production scheduling is a crucial task in modern steel plants. The scheduling of a wire rod and bar rolling process is challenging in many steel plants, which has a direct impact on their production efficiency and profit. This article studies a new single-machine scheduling problem with sequence-dependent setup time, release time, and due time constraints originated from a wire rod and bar rolling process in steel plants. In this problem, jobs have been assigned to batches in advance. The objective is to schedule the batches and jobs on continuous time to minimize the number of late jobs. A mixed-integer program is created as a baseline model. A baseline method is used to solve this NP-hard problem by solving the baseline model. We further design a two-stage decomposition method after analyzing the characteristics of this problem. Both actual and simulated instances with varying sizes are solved by using the proposed methods. The results demonstrate that the baseline method can only solve some small-scale cases, while the decomposition method can solve all small-scale cases and some medium-scale cases. Finally, we reveal the impacts of different instances on the performance of the proposed decomposition method. Note to Practitioners-This article deals with a new single-machine scheduling problem arising from an industrial wire rod and bar rolling process. A baseline method is given to tackle this problem by solving an established mixed-integer program. Afterward, a two-stage decomposition method is proposed such that an industrial size problem can be solved. Computational results of both actual and simulated cases show that it is more efficient than the baseline method in solving the scheduling problem. It can get an optimal solution of the concerned problem with one-week-scale batches and jobs in short time, thereby proving the readiness to put it in industrial use.

Industrial products' reuse, recovery, and recycling are very important because of their environmental and economic benefits. Effective product disassembly planning methods can improve their recovery efficiency and reduce their bad environmental impact. However, the existing approaches pay little attention to sequence-dependent disassembly with resource constraints, such as limited disassembly operators and tools, which makes the current planning methods ineffective in practice. This paper considers a multiobjective resource-constrained and sequence-dependent disassembly optimization problem with disassembly precedence constraints. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with traditional optimization criterion leads to a novel multiobjective optimization model such that the energy consumption and disassembly time are minimized while disassembly profit is maximized. Since the problem complexity increases with the number of components in a product, a lexicographic multiobjective scatter search (SS) method is proposed to solve the proposed multiobjective optimization problem. Its effectiveness is verified by comparing the results of linear weight SS and genetic algorithms. The results show that it is able to provide a better solution in a short execution time and fulfills the precedence requirement in a product structure and resource constraints.

Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs' processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines' abrasion and jobs' feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.

In this paper, we describe two meta-heuristic algorithms, a discrete cuckoo search algorithm (CS) and a genetic algorithm (GA), for finding of a near optimal solution of an NP-hard combinational sequence-dependent disassembly line-balancing problem. Both algorithms use population-based evolution strategies while they have different ways in exploring a search space. GA is inspired by natural evolution concepts and the search space is explored randomly, while in CS, strategical decisions are made to explore a search space and to update the individuals in a more efficient way. A comparison between two algorithms is performed, in terms of the number of optimal solution occurrences with a number of generations, and the results clearly show that CS algorithm outperforms GA in solving the concerned problem.

This paper studies a new scheduling problem in a steel plant, referring to continuous casting (CC), reheating furnace, and hot rolling (HR) processes, which is meaningful and important to the production efficiency and energy saving. First, the problem is modeled as a combination of two coupled subproblems: one assigns casts to continuous casting (CC) machines, decides sequence and start time for casts and rolling units; and another assigns furnaces and decides start time for rolling slabs in a reheating furnace. The objectives are to maximize the number of slabs processed in a mode of hot charge rolling or direct hot charge rolling so as to reduce the energy requirement and the temperature drop of slabs and minimize the residence time of slabs in a reheating furnace to save energy. Then, based on a Benders decomposition strategy, a hybrid algorithm that combines mixed-integer programming and constraint programming is designed to solve each subproblem. An effective cut-generation scheme based on a priority relationship is developed for resolving resource conflicts and unsatisfied setup time constraints. Finally, extensive experiments are conducted to verify the effectiveness of the proposed approach.

In order to study the effect of loss aversion behavior on multiperiod investment decisions, we first introduce some psychological characteristics of dynamic loss aversion and then construct a multiperiod portfolio model by considering a conditional value-at-risk (CVaR) constraint. We then design a variable neighborhood search-based hybrid genetic algorithm to solve the model. We finally study the optimal asset allocation and investment performance of the proposed multiperiod model. Some important metrics, such as the initial loss aversion coefficient and reference point, are used to test the robustness of the model. The result shows that investors with loss aversion tend to centralize most of their wealth and have a better performance than rational investors. The effects of CVaR on investment performance are given. When a market is falling, investors with a higher degree of risk aversion can avoid a large loss and can obtain higher gains.

In subway systems, kinetic energy can be converted into electrical one by using regenerative braking systems. If regenerative energy (RE) is fully used, the energy demands from power grid can be dramatically reduced. Since energy storage systems usually have a high cost, they are not considered in this work. Thus, RE has to be immediately utilized by accelerating trains; otherwise, it is wasted into heat via resistors. Timetable optimization methods are often used to coordinate accelerating and braking trains at a station, such that RE can be optimally used by the former. To improve RE utilization (REU) in a subway line, we propose a timetable optimization problem and establish its mathematical model. Many realistic constraints with the decision variables, i.e., headway time and dwell time, are considered. Then we design an improved artificial bee colony (IABC) algorithm to solve the problem. Several numerical experiments are conducted based on the actual data from a subway line in Beijing, China. The correctness of the mathematical model and effectiveness of IABC are shown by comparing it with commercial software CPLEX and a genetic algorithm, respectively. The impact of the decision variables on REU is analyzed, which helps to improve the timetable currently used in this subway line. We also test the robustness of the optimized timetable when certain disturbance takes place.

Industrial products' reuse, recovery and recycling are very important because of their environmental and economic benefits. Effective disassembly sequencing can improve recovery revenue and reduce environment impact. In this work, a stochastic dual-objective disassembly sequencing problem is established, which includes maximizing disassembly profit and minimizing energy consumption. Two popular and classical multi-objective evolutionary algorithms, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition, are used to deal with this important problem. By conducting simulation experiments on several numerical cases and analyzing experimental results with two well-known performance metrics, i.e., inverted generational distance and hypervolume, this work concludes that both can be used to obtain highly desired solutions.

Maximizing regenerative energy utilization (REU) through timetable optimization has become a hot topic recently. Considering the constraints of operation time for a subway system and travel time for each train, we propose a new timetable optimization problem to maximize REU. We formulate its mathematical model, and then an artificial bee colony (ABC)-based algorithm is designed to solve it. Case studies are conducted based on the actual data obtained from a real subway line. Experiments results prove the correctness of the mathematical model and effectiveness of the proposed ABC-based algorithm. The results are also used to improve the currently used timetable by reallocating its headway and dwell time properly. Impacts of decision variables on REU are discussed, which is useful for the timetable designers. In addition, the ABC-based algorithm is compared with GA and outperforms the latter.

The effective dismantling of discarded products regardless being used or not is critically important to their reuse, recovery, and recycling. However, the existing product disassembly planning methods pay little or no attention to resource constraints, e.g., limited numbers of disassembly operators and tools. Thus, a resulting plan when being executed may be ineffective in practice. This paper presents a dual-objective optimization model for selective disassembly sequences by considering multiresource constraints such that disassembly profit is maximized and time is minimized. A scatter search is adopted to solve the proposed dual-objective optimization model. It embodies the generation of diverse initial solutions, global assessment of objective functions, a crossover combination operator, a local search strategy for improved solutions, and a reference set update method. To analyze the effect of different weights on its performance, simulations are conducted on different products. Its effectiveness is verified by comparing its optimization results and those of genetic local search.

Disassembly modeling and planning are meaningful and important to the reuse, recovery, and recycling of obsolete and discarded products. However, the existing methods pay little or no attention to resources constraints, e.g., disassembly operators and tools. Thus a resulting plan when being executed may be ineffective in actual product disassembly. This paper proposes to model and optimize selective disassembly sequences subject to multiresource constraints to maximize disassembly profit. Moreover, two scatter search algorithms with different combination operators, namely one with precedence preserved crossover combination operator and another with path-relink combination operator, are designed to solve the proposed model. Their validity is shown by comparing them with the optimization results from well-known optimization software CPLEX for different cases. The experimental results illustrate the effectiveness of the proposed method.