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.

Query-based trip recommendation is an important task in location-based services (LBS), which aims to provide users with a sequence of points of interest (POIs) based on their queries. In trip recommendation, the effect of the visited POIs on the following decisions of users, called a forward effect, is mined by the existing studies. However, the effect of the following POIs on the previously visited ones, called a reverse effect, receives no attention. Therefore, this work proposes a graph-based double-layer bidirectional model for trip recommendation (GDB-TR), which is designed to mine both forward and reverse effects through bidirectional computation. Forward computation explores the influence of user’s history preferences on the next choice; reverse computation explores the influence of future goals on the current decision. Specifically, the model uses a heterogeneous graph to model users’ check-in trajectories with spatial and temporal information. Subgraphs are extracted from the heterogeneous graph, and an adjacency matrix is built for each subgraph. Vector representations of POIs and POI categories are obtained by fusing matrices based on a neural network. The double-layer bidirectional neural network is used to recommend a trip based on the user query, with one layer mining users’ preferences for POIs and the other layer mining the preferences for POI categories. Bidirectional computation is performed between the initial and destination nodes in each layer, capturing both forward and reverse effects. Specifically, a forward computation mines the influence of preceding POIs or POI categories on following ones, while a reverse computation does that reversely. Finally, experiments are conducted on five popular real data sets. The results show that GDB-TR outperforms all baseline models onF1and pairs-F1values, which validates the effectiveness of the proposed approach.

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.

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.

Due to their graphical representation and capability for property analysis, Petri nets (PNs) have been widely used in developing automated manufacturing systems (AMS). When designing them, it is important to perform their state reachability analysis and verify their functionality. The most common approach is to traverse a reachability tree of their PN models. However, it has the problem of state space explosion. A way to determine a state’s reachability is to find a firing sequence (FS) that corresponds to nonnegative integer solutions (NISs) of a state equation. Our prior work has given an algorithm to decide the existence of FS corresponding to an NIS in polynomial time. Yet it is impossible to decide the reachability of a marking given an infinite number of NISs of a state equation. This work studies the relationship between the PN model and NIS count of any state equation. An innovative method is proposed to modify a PN structure such that, for any given initial state and destination one, its state equation has no more than one NIS. Given an initial PN model of an AMS, by analyzing the relationship between initial PN properties and the modified one’s, we have that the proposed method can maintain the functionality of the modeled AMS. As a result, by using the modified PN as the AMS’s final model, the reachability of any marking can be determined in polynomial time, which can be viewed as a breakthrough result in the field of PN analysis. The proposed method is illustrated via case studies.

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.

In the realm of computational social systems, the ability to edit facial attributes accurately plays a crucial role in enhancing user experience on social media platforms and virtual environments. However, we face significant challenges in isolated attribute manipulation and balancing the tradeoff between editing fidelity and facial identity preservation. Here, this article presents a novel approach to constructing an orthogonal decomposition subspace, enabling precise editing control over individual attributes with minimal impact on others and maintaining identity consistency. We introduce an adaptive weight modulation (AWM) method and a maximum slope truncation (MST) formula. The AWM method, founded on a sufficient convergent criterion, performs singular value decomposition to yield subspace parameters that preserve rich facial knowledge within the generative model, facilitating high-quality facial generation with reduced parameterization. This empowers meaningful semantic interpretation of attributes, supporting diverse editing tasks such as pose, age, and eyewear adjustments. The MST formula rigorously defines the editing bounds to effectively navigate the tradeoff between editing depth and identity retention. We also propose a guideline for deciphering the specific meanings of unsupervised semantics, potentially advancing interpretability in social behavioral studies. An accompanying web application, available athttps://github.com/mickoluan/GreenLimeSia, has been developed, granting users the freedom to perform tailored facial edits. Extensive experimental results show we pave the way for more personalized and authentic interactions within computational social platforms.

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.

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.

Petri nets (PNs) are graphical and mathematical tools used to model a variety of discrete event systems and analyze their properties. Reachability is their fundamental property that is undecidable for PNs in general and has exponential complexity with respect to net size. One of the possible computation techniques of reachable states (markings) is based on the determination of legal firing sequences (LFSs) that correspond to a non-negative integer solutions (NISs) of a state equation. Our previous work has proposed an algorithm to decide the existence of LFSs corresponding to an NIS in polynomial time. However, it is impossible to check all NISs to decide a marking’s reachability in the case of an infinite number of NISs in a state equation. Hence, this work studies the relationship between the structure of a PN and the number of NISs of its state equation. Furthermore, an innovative method is proposed to modify its structure such that its state equation has no more than one NIS. The relationship between the properties of a PN and its modified one is studied in this article. We conclude that we can modify its structure while maintaining the function of the modeled system such that the reachability of the model can be decided in polynomial time. This can be viewed as a breakthrough result in the area of PN theory and applications. The correctness and time efficiency of the algorithm are verified by case studies and experiments. This work is important in putting PNs into the industrial use.

Electrocardiogram (ECG) signal classification is an important task in healthcare as it plays a vital role in early prevention and diagnosis of cardiovascular diseases. In this work, we propose an attention-based hybrid ResNet-Transformer network (ART-Net) for 12-lead ECG signal classification. It is comprised of a stacked multi-scale attention-based ResNet and self-attention-based Transformer. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by attention-based Resnet through signal segments, and attention mechanisms are used to adjust the weight of different channel features based on their importance. Next, these multi-scale features from a same ECG signal are integrated in chronological order as input to the Transformer network. In this end, extracting and fusing contextual information based on self-attention mechanism, and extracting the correlation between beats at different positions. The experimental results on CPSC2018 indicate that our model outperforms three state-of-the-art methods, and achieve 85.27% of accuracy, 86.01% of sensitivity and 85.59% of specificity, respectively.

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.

Two-Wheelers (TWs) such as bikes, e-bikes, and motorcycles often occupy lanes illegally and exceed speed limits, which leads to many traffic accidents. Therefore, we use deep reinforcement learning to design driving strategies for Autonomous Vehicles (AVs) to avoid collision with TWs and reduce injury of TW riders with irregular riding behaviors at un-signalized occluded intersections. First, the collision-avoidance behaviors of TWs are modeled, respectively. The state spaces integrate a safe avoidance range of AVs, a new position of AVs after taking deceleration and a steering angle, a predicted acceleration, and position, speed, and steering angle of AVs and other vehicles. At the same time, a reward function is designed based on the injury of TW riders and the driving safety and comfort of AVs. Secondly, a reinforcement learning model for autonomous driving strategy is constructed. Finally, Soft Actor-Critic is used to train the model, and the randomness policy is used to help AVs flexibly deal with the uncertain behaviors of TW riders and realize the balance between exploring unknown behaviors and using existing information. The simulation results show that compared with an autonomous emergency braking system, the injury of the riders using the driving strategy is reduced by 18.02% on average; compared with a risk-aware high-level decision strategy, the injury is reduced by 41.24% on average.

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.

Cooperative vehicle safety systems rely on periodic broadcasts of beacons to track positions and movements of concerned vehicles. In vehicular networking, vehicle driving environment is changing rapidly. This unique characteristic can cause dynamic network topology and heavy traffic conditions. In scenarios where traffic density is high, a large number of beacons could cause channel congestion, and the tracking performance of safety applications can thus be seriously impacted. To maintain high tracking accuracy for each node under varying traffic situations, this paper presents a distributed and coordinated channel access control strategy based on Multi-agent Model Predictive Control theory. First, we propose a multi-dimensional and hybrid Petri net model to characterize the interactions among multiple vehicles. The interaction model describes the possibility of collisions among vehicles. We then propose an application-dependent utility function that incorporates inter-vehicle collision behaviour. A model predictive control problem for beaconing rate adaption is formulated based on the function. Next, a distributed and coordinated decision-making scheme is designed. In this scheme, each node is treated as an agent. Each agent uses a model predictive control controller and coordinates with its neighboring agents to take channel access control actions. Simulation results validate that it improves channel resource utilization and tracking accuracy under dynamic driving situations.

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.

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.

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.

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.

Reachability is the basis for studying other dynamic properties of Petri nets (PNs). When a state equation is used to determine the reachability of a marking, we need to judge whether there is a corresponding legal firing sequence (LFS) for a non-negative integer solution (NIS), i.e., a firing count vector, of the state equation. The search for an LFS is an NP-hard problem, and previous work cannot always find an LFS for any NISs. This article proposes that transition-dependent circuits or firing-dependent circuits are the root cause that a state equation has an NIS but the marking is nonreachable, i.e., there is no LFS corresponding to an NIS in PNs. Based on this, we propose a state-equation-based backward algorithm (SBA) to determine whether there is an LFS corresponding to an NIS of the state equation in a PN. The correctness and effectiveness of SBA are verified by a case study on a PN-based flexible manufacturing system and through simulation on an S4PR net. The experimental results show that the time required for SBA to determine the existence of an LFS increases linearly with the transition firing count in NISs. When the number of NISs of a state equation is finite, we can efficiently determine the reachability of a marking. This represents an important result in theory and applications of PNs.

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.

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.

The intelligent transportation system has developed rapidly in recent years, and its reliability and safety have also attracted a lot of attention. The Adaptive Cruise control (ACC) system is a significant achievement of traffic intelligence. The principle of the ACC system is the process of balance feedback between the relative speed and distance of the front and current vehicles. In this paper, the running principle of ACC system is abstracted, and the Balanced Feedback Net (BFN) is proposed to model and analyze it based on Petri nets. The reachable marking graph and the incidence matrix of Petri nets are used to analyze the BFN model. The analysis results show a certain risk of rear-end collision in the balance feedback process of the ACC system. In this regard, we give a relevant risk identification algorithm to reduce the risk of rear-end collision and improve the reliability of the ACC system.

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.

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.

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.

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.

Process mining is an emerging technology used to extract, detect, and improve actual processes by extracting knowledge from event logs generated from information systems. In the production process, we can obtain the optimal process based on practical experience. Indirect dependencies may exist among different structures in the optimal process model discovered from the event log of the executions that perform better. However, the existing process mining algorithms cannot effectively mine the indirect dependencies among different structures. To compensate for this deficiency, an algorithm namedAlphaIDis proposed in this article, and it can mine the indirect dependencies in a loop-choice-driven loop structure. First, two algorithms are proposed to efficiently identify loop sequences and choice sequences from event logs. Then, the concept of association rules is proposed to describe indirect dependencies among different structures. Next, we expand the ordinary Petri net and redefine the new transition firing rules to represent the process model obtained byAlphaID. Finally, the correctness and effectiveness of the algorithm are verified by an artificial case and a real case.AlphaIDis integrated into the ProM which is an open-source process mining tool platform as a plug-in.

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.

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.

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.

Fairness and efficiency are two key requirements that have to be guaranteed in channel resource allocation in cooperative vehicle safety systems. Existing channel access control strategies, however, rely on each individual node to adjust networking parameters independently according to its locally measured state information, thus leading to unfairness. Although some coordinated strategies have been proposed to resolve this issue, they pay little attention to the efficiency. In order to achieve the tradeoff between fairness and efficiency, in this paper, we propose a utility function in terms of inter-packet reception time required to capture the performance of consecutive successful packets' reception under various vehicle densities. A channel access control problem among vehicles is then formulated as a non-cooperation game model. This model utilizes a punishment function to penalize a node that monopolizes the channel resources and hence can enable nodes to coordinate with each other to achieve desired fairness and efficiency. Next, a distributed decision-making scheme for channel access control is designed. It adjusts transmission rate in a coordinated manner and can guide each node to reach a Pareto-optimal Nash equilibrium point. The experimental results validate that the proposed strategy can result in fair channel resource allocation and ensure high-tracking accuracy for each vehicle under dynamic traffic conditions.

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.

Exploration and discovery of the relationship between social media activities and rare-event evolution have been investigated by many researchers in recent years. Their investigations have revealed the existence of such relationship. Furthermore, some researchers regard finding either a temporal or spatial pattern of social media activities as a way to evaluate the evolution of rare event. However, most of them fail to deduce an accurate time point when a rare event highly impacts social media activities. This paper concentrates on the intensity of information volume and proposes an innovative data processing method based on clustering algorithms. The proposed method can characterize the evolution of a rare event in the real world by analyzing social media activities in the virtual world. This exploration contributes to study changes of social media activities in the time domain. A case study is based on Hurricane Sandy that occurred in 2012. Social media data collected from Twitter during its arrival time span are adopted to evaluate the feasibility and effectiveness of our proposed method. First, this paper confirms that a strong correlation between a rare event and social media activities does exist. Next, it uncovers that a time difference does exist between the real and virtual worlds. In general, this paper gives a novel idea that deduces a temporal pattern of social media activities during the occurrence of rare events.

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.

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.

This work designs a two-level strategy at signalized intersections for preventing incident-based urban traffic congestion by adopting additional traffic warning lights. The first-level one is a ban signal strategy that is used to stop the traffic flow driving toward some directions, and the second-level one is a warning signal strategy that gives traffic flow a recommendation of not driving to some directions. As a visual and mathematical formalism for modeling discrete-event dynamic systems, timed Petri nets are utilized to describe the cooperation between traffic lights and warning lights, and then verify their correctness. A two-way rectangular grid network is modeled via a cell transmission model. The effectiveness of the proposed two-level strategy is evaluated through simulations in the grid network. The results reveal the influences of some major parameters, such as the route-changing rates of vehicles, operation time interval of the proposed strategy, and traffic density of the traffic network on a congestion dissipation process. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

Recently, some researchers attempt to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of them can accurately deduce a time point when social media activities are highly affected by a rare event. Thus, it is difficult to characterize an accurate temporal pattern of social media during the evolution of a rare event. This work proposes an innovative method to characterize the evolution of a rare event by analyzing social media activities. We find that there is a time difference between the event and social media activities in a time domain. This is conducive to investigate the temporal pattern of social media activities. The proposed method focuses on the intensity of information volume by adopting a clustering algorithm. Our case study focuses on a hurricane named Sandy in 2012. Twitter data collected around it is used to verify the effectiveness of the method. The results not only verify that a rare event and social media activities have strong correlation, but also reveal that they have a time difference. This work provides an effective and reliable method to find a temporal pattern of social media when a rare event occurs.

This paper proposes a methodology to categorize drivers' behaviors at a congested signalized intersection. As a discrete event system model, timed Petri nets (TPNs) are used in this paper to formally define two kinds of behaviors: non-jam-induced driving behavior and jam-induced one. In order to systematically assess the performances of both behaviors, a new urban traffic network model is built: a cell transmission model is used to depict the road link traffic that is consistent with the kinematic property of traffic flow, and TPNs are used to model the behaviors and the conflicting traffic flow at the intersection. Some simulation results are given to evaluate the impact of driving behavior on the traffic delay.

Petri nets (PNs) are well utilized as a visual and mathematical formalism to model discrete-event systems. This paper uses deterministic and stochastic PNs to design an emergency traffic-light control system for intersections providing emergency response to deal with accidents. According to blocked crossing sections, as depicted by dynamic PN models, the corresponding emergency traffic-light strategies are designed to ensure the safety of an intersection. The cooperation among traffic lights/facilities at those affected intersections and roads is illustrated. For the upstream neighboring intersections, a traffic-signal-based emergency control policy is designed to help prevent accident-induced large-scale congestion. Deadlock recovery, livelock prevention, and conflict resolution strategies are developed. We adopt a reachability analysis method to verify the constructed model. To our knowledge, this is the first paper that employs PNs to model and design a real-time traffic emergency system for intersections facing accidents. It can be used to improve the state of the art in real-time traffic accident management and traffic safety at intersections.

Logical Petri Nets (LPNs) can well describe and analyze batch processing functions and passing value indeterminacy in cooperative systems. Their structures are simpler than the equivalent inhibition Petri nets. To analyze them, a vector matching method has been given. In this paper a subclass of LPNs is proposed and its properties are analyzed. It can be used to model and simulate e-commerce systems well. Its special structures and relationships among its logical transitions are discussed. Based on them, a reachability tree is constructed and the state equations are used to analyze its properties, that is, reachability, liveness, conservativeness, and reversibility. The proposed analysis method significantly lowers the computational efforts in comparison with the existing method for LPNs.

Petri nets (PNs) are well utilized as a visual and mathematical formalism to model discrete event systems. Some extensions to PNs enhance their modeling capability. This work uses Time Petri nets (TPNs) and Synchronized Petri nets (SPNs) to design a traffic control system (ITCS) for intersections dealing with accidents such that emergency response is provided and additional accidents are prevented. It includes a camera surveillance subsystem (CSS) and a traffic light control subsystem (TLCS) simulated by using SPNs and TPNs, respectively: a multi-camera surveillance mechanism is established to sense and detect the accident, synchronously, and then according to the information of the accident, corresponding traffic light control policies are carried out to prevent additional accidents from happening. A reach ability tree method is adopted to demonstrate how the models are used to enforce the phase of traffic transitions, and verify their important properties. To our knowledge, this is the first work that employs PNs to model and design the real-time traffic control system for intersections facing accidents, and to perform the cooperation of cameras and traffic lights. This helps enhance the state of the art in real-time traffic accident detection and traffic safety at an intersection.