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.

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

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

As an effective approximation algorithm for multi-objective jobshop scheduling, multi-objective evolutionary algorithms (MOEAs) have received extensive attention. However, maintaining a balance between the diversity and convergence of non-dominated solutions while ensuring overall convergence is an open problem in the context of solving Multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs). To address it, we propose a new MOEA named MOEA/DCH by introducing a hierarchical estimation method, a clustering-based adaptive decomposition strategy, and a heuristic-based initialization method into a basic MOEA based on decomposition. Specifically, a hierarchical estimation method balances the convergence and diversity of non-dominant solutions by integrating Pareto dominance and scalarization function information. A clustering-based adaptive decomposition strategy is constructed to enhance the population’s ability to approximate a complex Pareto front. A heuristic-based initialization method is developed to provide high-quality initial solutions. The performance of MOEA/DCH is verified and compared with five competitive MOEAs on widely-tested benchmark datasets. Empirical results demonstrate the effectiveness of MOEA/DCH in balancing the diversity and convergence of non-dominated solutions while ensuring overall convergence.

Digital twin (DT) is a novel technology with a promising prospect in smart manufacturing. It can realize high-fidelity mapping of physical entities, data fusion of physical and virtual space, and real-time monitoring of physical entities. This makes it easy to make optimal decisions and precise control. The steel industry has long production processes, complex equipment operation mechanisms, and dynamic production environment. Due to the lack of digital representation of its production process, many optimization and control schemes obtained by existing methods have large deviations in their implementation processes. How to provide accurate digital representation and make optimal decisions based on it is a great challenge. The development of DT technology in industrial scenarios provides a solution to this issue. In the steel production process, reheating furnaces are highly energy-consuming facilities connecting the continuous casting and hot rolling processes. This work proposes a five-dimension DT faced to parallel walking beam reheating furnaces. In it, we construct a three-stage model for virtual entities. It can accurately obtain the charging/discharging times and real-time positions of slabs, along with simulating the slabs’ logistics processes. An adaptive large neighborhood tabu search algorithm based on DT, named as ALTS-DT, is further proposed for parallel reheating furnace scheduling (PRFS) problems. By conducting numerous computational experiments, we verify the following: first, the high accuracy of the proposed three-stage model of DT, with relative error under 0.98%; second, the vital role of DT in PRFS; third, the excellent performance of the proposed ALTS-DT in solving actual PRFS problems.

Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switch bring great challenge for high-performance production scheduling in BPSs. In this study, we present a novel batch scheduling problem derived from a satellite vibration test system. Different from basic scheduling problems in BPSs with tool switching, it has complications caused by many-to-many task-tool matching and job release time constraints. Specifically, each job has predetermined candidate tools. One of them should be selected and equipped to process a given job. Unlike sequence-dependent setup time that is only related to two adjacent jobs, tool switching time is additionally dependent on the tools selected by both jobs from their respective candidate tools. Both job sequence and tool selections need to be determined to minimize makespan. Mixed-integer linear programming (MILP) and constraint programming (CP) models are developed for formulating the problem. We prove that the problem isNP-hard. To obtain exact solutions, we propose a novel branch-andbound algorithm (B&B) based on the analysis and extraction of problem properties. It integrates a domain reduction mechanism, branching strategies, and dominance rules. Among them, the domain reduction mechanism reduces the number of jobs to be scheduled; the branching strategies accelerate solving speed; and the dominance rules save the memory consumption. Ablation studies and key influencing factor experiments are performed. The results show that the proposed B&B can optimally solve much larger instances than state-of-the-art methods (including some heuristics). It can optimally solve industrial-size problems in a short time, implying that it can well satisfy the performance demand of a satellite vibration test system and thus advancing the filed of optimal scheduling for BPSs.

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

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

Rational allocation of resources can improve the profit margin of a steel enterprise. This paper deals with a multi-product multi-stage multi-period resource allocation problem. In it, product manufacturing involves multiple continuous production stages, each of which has parallel machines. According to process requirements, the tasks assigned to a machine need to be produced in batches. The process route of a product is a sequential combination of machines each of which is to be selected from a stage. The process route for each product and the batching rules of each machine are known in advance. Multi-period production means that the tasks released before a planning period can be processed in any of its periods. The demand for each product type in each period and the capacity of each machine are predetermined. Considering a customer’s demand, we optimally allocate machines for products in each planning period to achieve their efficient utilization. The objective is to minimize the sum of various costs related to transportation, resources, unmet demand, and product inventory. A mixed integer linear program is developed for the concerned problem. A fix-and-optimize heuristic with variable neighborhood size is newly designed to obtain high-quality solutions. Its solutions are compared with those of CPLEX (a commercial software) given a fixed solution time. Experimental results show that it can accurately solve small-scale instances and find better solutions than CPLEX for most large-scale instances. Comparison experiments are conducted and the results show that the proposed algorithm has excellent accuracy, speed, and stability in addressing the concerned problem. Note to Practitioners—As demand for steel products gradually shows a trend towards multiple varieties, small batches, and personalized customization, it increases the difficulty for practitioners to rationally allocate resources for their production in a steel enterprise. It is hard to achieve rational material and machine resource allocation subject to complex constraints for processing multiple products in multiple production stages and periods. To deal with a multi-product multi-stage multi-period resource allocation problem, it is essential to design efficient and stable algorithms. A fix-and-optimize heuristic with variable neighborhood size is thus proposed for addressing it. The method can decompose the problem into a series of subproblems according to a decomposition scheme. They are iteratively solved. In this work, our goal is to help practitioners to deal with the challenging resource allocation problem in a short time. The effectiveness of the proposed algorithm is validated and tested by comparing its results with those of a commercially available exact solver called CPLEX on various problem instances. Extensive experimental results demonstrate its effectiveness. It can quickly solve small-scale instances with no statistically significant difference from the optimal solutions obtained by CPLEX. When addressing large-scale instances, the proposed algorithm shows better solution performance than CPLEX in a given running time. The algorithm is flexible, accurate, and fast, which implies its great application potential for resource allocation in steel enterprises.

Path finding in multi-agent systems aims to identify collision-free and cost-optimized paths for all agents with distinct start and goal positions. It poses challenging optimization problems. Existing research typically treats all agents equally, overlooking their differences in practical scenarios where they undertake the tasks of varying importance. In many application scenarios, agents must be differentiated into critical (c-agents) and acritical ones (a-agents) due to premium/general service, no-loaded/full-loaded states, and urgent/non-urgent tasks. Facing this practical need, this work focuses on multi-agent systems in which the different importance of agents must be considered; and tackles a lexicographic dual-objective variant of path-finding problem. The consideration of c-agents makes the concerned problem more useful yet more challenging than basic multi-agent path-finding problems. Different from existing multi-agent path planning methods that minimize the sum-of-costs of all agents, we optimize two objectives with preferences to emphasize the influence of c-agents on the system. The primary one is to minimize the sum-of-costs of c-agents and the secondary one is to minimize that of a-agents. As existing methods are inadequate for this unique challenge, we adapt a conflict-based search framework and design new two-level lexicographic dual-objective optimization methods to deal with it. A high level is responsible for iteratively expanding a search tree and adding constraints to resolve conflicts among agents. A low level is responsible for finding the path of each agent for the node newly expanded in the high level. By conducting numerous computational experiments, we verify the great performance of the presented methods in solving the concerned problem. We further develop a prototype system incorporating our methods and make it public to promote their practical application. This research contributes valuable insights and solutions to pathfinding challenges in multi-agent systems with critical and acritical agents. Note to Practitioners—This work addresses a multi-agent path finding problem in multi-agent systems involving both critical and acritical agents. The former are more important than the latter since they are assigned to perform more important tasks. This is a common scenario in manufacturing and service environments. We propose a two-level lexicographic dual-objective optimization framework to deal with the problem and underscore the importance of c-agents in the path finding problem. Three solution approaches are designed for practitioners to select based on their practical application needs. The computational experimental results and statistical analyses highlight the exceptional performance of our proposed approaches. In order to facilitate the practical application, we further provide a prototype system with our proposed approaches embedded, which is openly accessible for practitioners to realize their specific applications.

A production workshop with mobile robots can be considered as a hybrid system consisting of a production system and a transportation one. Mobile robots are responsible for transferring production tasks among the machines of a production system and constitute a multi-robot transport system. It is highly coupled with a production system because of the interdependency that exists between production scheduling and mobile robot assignment. In this work, we study their integrated optimization problem for a mobile robot-based job shop with blocking properties. Its aim is to minimize total completion time as an objective function to improve overall operational efficiency. We consider the speed of a robot that varies according to whether it is loaded or not. We formulate this new problem into a mixed integer linear program to provide an algebraic description. Then, we propose a constraint programming method to solve it with high efficiency. The superiority of constraint programming over mixed integer linear programming in terms of the number of variables and constraints is analyzed. Numerous experiments on benchmark examples show that constraint programming can well handle the concerned problem. Under a one-hour time limit, it can exactly solve its instances while mixed integer linear programming cannot. Under a one-minute time limit, it obtains much better solutions than mixed integer linear programming and heuristic strategies, thus implying its high potential to be put into industrial applications. Note to Practitioners—The integration of mobile robots into production workshops has emerged as a pivotal strategy to enhance the operational efficiency of an advanced manufacturing system. This integration transforms the traditional job shop into a hybrid system, where mobile robots play a crucial role in transferring production tasks among machines. This brings a unique challenge to practitioners due to the intricate interdependencies between production scheduling and mobile robot assignment. The focus of our study is on the optimization of a mobile robot-based job shop to improve its overall operational efficiency. Our approach involves formulating this complex problem as a mixed-integer linear program, thereby providing a concise mathematical representation. We propose a constraint programming method to solve the problem efficiently. Through numerous experiments on benchmark examples, our findings indicate that the proposed constraint programming method can well solve the concerned problem given long or short solution time. This underscores its high potential for practical implementation in industrial scenarios.

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.

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

In the era of booming e-commerce and Internet of Things technology, robotic mobile fulfillment systems (RMFSs) have gained more and more use in logistics industry. While these systems enhance labor efficiency, they introduce numerous optimization challenges. Order picking is a human–robot collaborative process in RMFS. It involves three critical and interrelated operational optimization issues: 1) PS; 2) resource scheduling; and 3) manual picking. Each of them is an NP-hard combinatorial optimization problem. Their integration is a significant challenge for operational optimization in RMFS with multiple picking stations and represents a novel problem that was not studied before to our best knowledge. To tackle this complex problem and fill the research gap, we first model it as a MIP to derive exact solutions for small-scale and illustrative cases. For industrial-scale cases that cannot be solved by exact methods given limited time, we propose a tailored learning-strategies-enhanced local search algorithm. It integrates a 3-D bi-section encoding strategy, a three-stage decoding policy, a learning-based ANS method, and two learning-based tabu mechanisms. Experimental results demonstrate the effectiveness of our proposed method, achieving 2.9% to 33.2% performance improvement over six competitive peers. This highlights its superiority in solving the concerned problem, providing significant potential for addressing practical order picking optimization challenges in RMFS.

A Robotic Mobile Fulfillment System (RMFS) is an emerging “cargos-to-person” picking system that relies on the broom of Internet of Things (IoT) technology. It aims to offer significant enhancements in order picking efficiency. However, dynamic disturbances, such as inventory discrepancies arising from errors in receiving, shipping, and handling of goods, often disrupt its operations, leading to degraded service and increased operational costs, thereby affecting overall system performance. Traditional optimization solutions may necessitate adaptations or overhauls in response to such disturbances. This paper introduces a proactive multi-pathway response algorithm tailored to mitigating dynamic disturbances in RMFS, particularly concerning inventory discrepancies. We extend an open-source simulation framework to evaluate the performance of the proposed algorithm and conduct a comparative analysis of dynamic systems. Experimental results indicate that our proposed algorithm can effectively improve the processing efficiency of abnormal orders with the minimal system-wide impact, highlighting its potential to well address dynamic and abnormal events in smart warehouses.

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice.

With the development of robotics and Internet of Things, robot-assisted goods-to-person order picking systems become popular in smart warehouses. Order picking in such systems is a human-robot collaborative process, where robots carry pods to a picking station with human pickers who pick the demanded goods from them to fulfill orders. In it, pod selection, robot scheduling, and manual picking are highly coupled and together influence the efficiency of order picking. Their joint optimization is the key to enhancing operational efficiency but rarely studied in existing work. In order to fill such a research gap and meet high market demand, this work focuses on a novel human-robot collaborative order picking optimization problem. A mixed integer program is formulated to model it and provide an exact solution method for small-scale instances. To provide large-scale problems with efficient solutions in practical application scenarios, we propose an adaptive large-neighborhood-based tabu search algorithm. Specifically, an adaptive large neighborhood search method is designed and embedded into a tabu search algorithm with two tabu mechanisms. Experimental results indicate that the presented algorithm has significant advantages in solving the newly proposed problem. It substantially outperforms: 1) the independent use of adaptive large neighborhood search or tabu search, 2) Gurobi subject to an hour execution time, and 3) several competitive benchmark and newest well-performing algorithms. Its high performance implies its great potential in solving practical order picking optimization problems for Internet-of-Things-enabled robot-assisted smart warehouses.

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

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

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

Batch scheduling problems are NP-hard and often coupled with logistics optimization problems in industrial manufacturing scenarios, further increasing the challenge of decision-making. This work focuses on a stainless steel hot-rolling production process, where it is essential to maintain the temperature of the products from the previous process beyond a certain threshold. Our work diverges from typical hot-rolling production processes by necessitating the utilization of alternative thermal devices, thereby intricately linking batch scheduling with logistics optimization and resulting in a novel lexicographical dual-objective optimization problem. To address this complex problem, we first introduce a mathematical model formulated as a mixed integer program, providing exact solutions of the concerned problem but taking much computation time. To provide effective and efficient solutions, we then propose an enhanced simulated annealing algorithm, which integrates destruction and construction methods inspired by iterated greedy algorithms. This algorithm is tailored to the specific characteristics of the problem, incorporating specialized encoding-decoding mechanisms, neighborhood search operators, and a Metropolis acceptance criterion. Our experimental results highlight the effectiveness of proposed approaches, demonstrating their superiority over competitive peers. Thus, this research contributes valuable insights and innovative solutions to the scheduling and optimization challenges inherent in batch manufacturing processes with temperature constraints and thermal devices.

An oversampling technique balances a dataset by increasing the number of minority samples. It is a common and effective method in imbalanced learning. However, most oversampling methods have randomness in generating minority samples, which would have negative impacts on the prediction performance of subsequent classifiers. This study treats the prediction made by classifiers as a black-box optimization problem. The optimization objective is to improve the classification accuracy of subsequent classifiers for minority samples. The solution of this optimization problem can be regarded as a minority sample that can be and added to the imbalanced dataset. The minority samples are iteratively generated by Bayesian optimization (BO). We determine two valuable intervals for each 1-D continuous variable feature. One is the interval with the densest minority samples. The other is that with the sparsest majority samples distributed among the minority samples. By adjusting the proportion of samples generated in the two areas, the presented algorithm can be flexibly applied to different datasets. In order to reduce the noise that may be caused by the exploration phase of BO, a sample selection procedure is carried out to eliminate the samples that are worse than those generated at the previous iteration. The samples generated in this way are based on the principle of improving the performance of the classifier, thus avoiding the negative effects of randomness. Experimental results via twenty open imbalanced datasets show that the proposed method obtains better results than existing state-of-the-art oversampling models, thus well advancing the important field of imbalanced learning.

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.

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.

In the manufacturing process of aluminum alloy, the size, distribution, and shape of microscopic grains indicate the mechanical characteristics and product quality. However, for metallographic images that can reveal microstructures, the cost of expert labeling at pixel level is high. To solve the problem, we propose a semisupervised learning strategy for grain boundary detection with a few labeled images and abundant unlabeled samples. To expand the helpful information, transfer learning and rule-based region growing are considered. Specifically, a deep network used for extracting multiscale features is designed. With constant training, through a few labeled metallographic images and abundant transferred natural images, pseudo annotations are generated gradually for unlabeled metallographic images iteratively by feature similarity and boundary region growing. The increased unlabeled samples with their pseudo annotations would be involved in the following training process in semisupervised self-training mode to improve the generalization ability of model, together with the domain adaptation block. In experiments, the proposed two methods named semiricher convolutional features-generative adversarial networks (SemiRCF-GAN) and semiricher convolutional features-maximum mean discrepancy (SemiRCF-MMD) can effectively detect grain boundaries with only one labeled metallographic image, and achieve F1 scores of 0.73 and 0.72, respectively, which surpass typical methods.

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

Multi-Agent Path Finding (MAPF) is an important problem with a variety of applications. Its aim is to find collision-free paths for agents having separate start and goal positions. This work proposes a new lexicographic bi-objective MAPF considering different task types, where agents are divided into two kinds to perform critical and acritical tasks. This is common in practical intelligent warehousing scenarios where a critical/acritical-task-performing agent (called c-agent and a-agent, respectively) may represent a full-load/no-load one or the one conducting urgent/non-urgent tasks. The primary objective is to minimize the sum-of-costs of c-agents, while the secondary objective is to minimize the sum-of-costs of a-agents. Two MAPF algorithms are modified to fit and solve the concerned problem for the first time. Moreover, four improvement strategies are embedded to the proposed algorithms and proved to be effective in solving MAPF problems with different task types.

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

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

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

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

An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems. IGA was first developed to solve an FSP in 2007. Since then, various FSPs have been tackled by using IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. Up until now, over 100 articles related to this field have been published. However, to the best of our knowledge, there is no existing tutorial or review paper of IGA. Thus, we focus on FSPs and provide a tutorial and comprehensive literature review of IGA-based methods. First, we introduce a framework of basic IGA and give an example to clearly show its procedure. To help researchers and engineers learn and apply IGA to their FSPs, we provide an open platform to collect and share related materials. Then, we make classifications of the solved FSPs according to their scheduling scenarios, objective functions, and constraints. Next, we classify and introduce the specific methods and strategies used in each phase of IGA for FSPs. Besides, we summarize IGA variants and hybrid algorithms with IGA integrated, respectively. Finally, we discuss the current IGA-based methods and already-solved FSP instances, as well as some important future research directions according to their deficiency and open issues.Note to Practitioners—Many practical scheduling problems can be transformed into flow-shop scheduling problems (FSPs), most of which are NP-hard. In order to solve them in an industrial system setting, designing effective heuristics is important and practically useful and has, thus, attracted much attention from both researchers and engineers. As an easy and high-performance heuristic, an iterated greedy algorithm (IGA) is widely used and adapted to solve numerous FSPs. Its simple framework makes it easy to be implemented by practitioners, and its high performance implies its great potential to solve industrial scheduling problems. In this work, we aim to give practitioners a comprehensive overview of IGA and help them apply IGA to solve their particular industrial scheduling problems. We review the papers that solve FSPs with IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. First, we provide practitioners with a tutorial on IGA, where an example for solving an FSP is introduced and an open platform is constructed. The platform collects and shares the related materials, e.g., open-source code, benchmarks, and website links of important papers. Then, we introduce various FSPs and specific designs of IGA-based methods. Finally, we discuss the current research and point out future research issues.

In modern logistics and smart warehouse, a terminal exists as a hub to connect multiple transportation modes and exchange goods. Solving a block relocation problem (BRP) arising from block retrieval processes in a terminal is fundamentally important to enhance the terminal’s overall efficiency and save its energy. In this paper, we improve the state-of-the-art mixed integer programming (MIP) formulation of an unrestricted BRP by extracting valid inequalities from structural properties and proposing a new variable fixation method to solve it. Computational results show that the improved model can be optimally solved much more easily than the original model. Among over five-hundred benchmark instances with height limits, the improved model can solve 12.3% more than the original one. For the instances that can be optimally solved by the latter, the improved model shows six times faster speed than the latter. This work represents a significant advance in this important area.

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.

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.

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.

Batch scheduling problems deal with jobs to be processed in batches in many industrial production systems. They are hard to solve. This work proposes a novel bi-objective batch scheduling problem with the constraints of release time and sequence-dependent setup time. As an important characteristic of the concerned problem, the number of late jobs within a batch varies with its start time. A mixed-integer linear program is proposed to describe this problem. Two objectives, i.e., minimizing the total number of late jobs and setup time, are considered. Two memetic algorithms by integrating a non-dominated sorting genetic algorithm II (NSGA-II) and 2-opt local search are designed to solve the concerned problem. They adopt different crossover operators, i.e., partially mapped one and precedence preserved one. By comparing the results of the proposed algorithms with their peers on extensive experiments, we conclude that the proposed algorithms get much better Pareto fronts than their peers at the expense of more execution time. Yet, their speeds are fast enough to solve the problems with industrial scales and thus prove the readiness to put them in industrial use.

The block relocation problem (BRP) is a fundamental operational issue in modern warehouse and yard management, which, however, is very challenging to solve. In this article, to advance our understanding of this problem and to provide substantial assistance to practice, we adopt the following: 1) introduce a classification scheme and present a rather comprehensive review on all 16 BRP variants; 2) develop a general framework to derive lower bounds on the number of necessary relocations and demonstrate its connection to existing lower bounds on the unrestricted BRP variants; 3) propose and employ a couple of new critical substructure concepts to analyze the BRP and obtain a lower bound that dominates all existing ones; 4) build a new and strong mixed integer programming (MIP) formulation that is adaptable to compute eight BRP variants, and design a novel MIP-formulation-based iterative procedure to compute exact BRP solutions; and 5) extend the MIP formulation to address four typical industrial considerations. Computational results on standard and practical test instances show that the new lower bound is significantly stronger, and our new MIP computational methods have superior performances over the state-of-the-art formulation and a heuristic adopted in a steel plant.

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.

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

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