The flexible job shop scheduling problem with limited automatic guided vehicles (FJSP-AGV) is prevalent in manufacturing enterprises. To improve production efficiency and reduce energy consumption, this paper investigates the energy-efficient FJSP-AGV (EFJSP-AGV), aiming to minimize both the makespan and total energy consumption. To address EFJSP-AGV, both exact and approximate methods were developed. The exact method employs a novel mixed integer linear programming (MILP) model, capable of producing optimal Pareto solutions for small-sized instances using the epsilon method. EFJSP-AGV is an NP-hard problem that involves three subproblems: operation sequencing, machine selection, and AGV selection. To overcome these challenges, a novel approximate method called imitation learning (IL)-assisted multi-population evolutionary algorithm (ILMPEA) was proposed. The multi-population evolutionary framework assigns distinct search regions to populations to improve the efficiency of solution space exploration. To further enhance search accuracy, IL is applied to select search operators, guiding the Pareto front toward a better approximation of the true front. Experimental results demonstrated the effectiveness of both the MILP model and ILMPEA.

With the increasing number of motor vehicles per capita, the problem of road traffic congestion is becoming increasingly prominent. Intelligent transportation systems are playing an increasingly important role in traffic management. Establishing a reasonable and effective traffic congestion prediction model is crucial for the implementation of intelligent transportation systems. This article proposes an end-to-end congestion prediction framework for unsupervised learning. First, a traffic data deep embedding clustering model based on contrast learning (CL-DEC) was constructed to discretize and label the congestion status of spatiotemporal rasterized GPS data. Then, the Time Convolutional Network (TCN) is used to extract features from the labeled data, and the Dependency Matrix Attention Mechanism (DMAttention) is combined to highlight important features for predicting traffic congestion. In this study, we found that DEC can effectively alleviate the false positive problem of CL, and the combination of the two improves discriminability and comparability, which helps to improve the accuracy of data annotation. In addition, TCN-DMAttention can balance the short-term fluctuations and long-term trends of traffic time series data. We compared it with 13 baseline models and conducted robustness analysis using 20 random number seeds. The experimental results indicate that the TCN-DMAttention model has better predictive performance and good stability than other comparative models do.

Due to the impact of economic globalization, distributed welding shop has become prevalent in real-world manufacturing systems. Moreover, focusing on human-centric, sustainable and resilient industry, Industry 5.0 puts more emphasis on human-robot collaboration (HRC) for its merit in promoting system flexibility and adaptability. However, owing to the instability of human performance, it becomes necessary to employ fuzzy processing time to simulate practical human production. In the context of Industry 5.0, HRC scheduling in distributed mixed fuzzy welding shop is worth exploring, but no related research on this problem is reported. Thus, to address this research gap, this paper investigates a human-robot collaboration energy-efficient distributed mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC), aiming to minimize makespan and total energy consumption (TEC). To solve this issue, a self-learning memetic algorithm (SLMA) is proposed. In SLMA, a hybrid initialization is designed to yield a high-quality initial population. A genetic operator is proposed to improve the exploration capability. A self-learning variable neighborhood search (SLVNS), which hybridizes Q-learning and VNS, is developed to enhance the exploitation capability. A resource adjustment strategy is presented to further optimize TEC. Additionally, to validate the effectiveness of the proposed SLMA, extensive experimental comparisons with 5 other optimization algorithms are conducted. Experimental results illustrate that SLMA outperforms its competitors. Note to Practitioners—Owing to the widespread presence in manufacturing systems, distributed welding shop has attracted considerable attention in both industry and academia. In the context of Industry 5.0, the incorporation of human-robot collaboration (HRC) scheduling in distributed welding shop can promote system productivity and flexibility. Meanwhile, due to the instability of human performance, employing fuzzy processing time to simulate human production more aligns with the practical manufacturing scenario. Thus, this paper investigates a human-robot collaboration energy-efficient distributed mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC). This problem model can be utilized in many welding manufacturing enterprises with HRC production mode. To solve this problem, we design a self-learning memetic algorithm (SLMA) to minimize both makespan and total energy consumption (TEC). The design of all components in SLMA is based on the characteristics of problem. The SLMA can offer the low-energy and high-efficiency schedules for practitioners. Experimental results verify the effectiveness of the proposed SLMA.

This article studies the flexible job shop scheduling problem with a certain number of automatic guided vehicles (FJSP-AGVs), aiming to minimize the makespan. First, a novel constraint programming (CP) model is formulated to obtain optimal solutions. Specifically, the proposed CP model addresses the shortcomings of the existing CP model, which cannot solve instances with a machine processing two consecutive operations of the same job. Additionally, redundant and symmetry-breaking constraints are designed to accelerate constraint propagation and break problem symmetry, respectively. Then, to more effectively solve FJSP-AGVs, a CP-assisted meta-heuristic algorithm framework is designed, with a CP-assisted dual-population collaborative genetic algorithm (DCGA-CP) being developed as an example. Finally, experiments are performed on benchmark instances to demonstrate the effectiveness and superiority of the proposed CP model and DCGA-CP. Experimental results show that the proposed CP models first prove 29 new optimal solutions and improve 27 best-known solutions. Meanwhile, DCGA-CP first proves 29 new optimal solutions and improves 32 best-known solutions for benchmark instances.

Human–robot collaborative scheduling has been widely applied in modern manufacturing industry. A rational scheduling of human–robot cooperation plays an important role in improving production efficiency. However, human–robot collaborative scheduling problem in welding production has not been studied so far. Thus, this article addresses a human–robot collaborative welding shop scheduling problem (HCWSSP) with minimization objectives of makespan and total energy consumption (TEC). To solve this multiobjective HCWSSP, a Pareto-based memetic algorithm (PMA), which hybridizes a genetic operator and variable neighborhood search (VNS), is presented to obtain a set of tradeoff solutions between makespan and TEC. In PMA, each solution is represented by two parts, i.e., job processing sequence and resource assignment. A novel integrated initialization strategy is proposed to generate one initial population with high quality and good diversity. Furthermore, five kinds of VNS are designed to improve the exploitation capability of PMA. Experimental results on test problems manifest that the proposed PMA performs better than its competitors.

Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners-There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators.

This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines with different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize the makespan and total energy consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. To solve this problem, a three-stage multiobjective approach based on decomposition (TMOA/D) is suggested, in which each solution is bound with a main weight vector and a set of its neighbors. Accordingly, a variable direction strategy is developed to ensure each solution along its main direction is thoroughly exploited and can jump to the neighboring directions using a proximity principle. To ensure an active schedule of arranging jobs to machines, a two-level solution representation is employed. In the first phase, each solution attempts to improve itself along its current weight vector through a developed neighborhood-based local search. In the second phase, the promising solutions are selected through the technique for order preference by similarity to an ideal solution. Then, they attempt to update themselves with a proposed global replacement strategy via incorporation with their closing solutions. In the third phase, a solution conducts a large perturbation when it goes through all its assigned weight vectors. Extensive experiments are conducted to test the performance of TMOA/D, and the results demonstrate that TMOA/D has a very competitive performance.

This paper studies a hot rolling batch scheduling problem in compact strip production (CSP), which is decomposed into a two-stage problem. The first stage is the strip combination problem aimed at determining the strip combination of each rolling turn and the number of rolling turns with the objective of minimizing the number of virtual strips, and the second is the strip allocation and sequencing problem aimed at optimizing the allocation and rolling sequence of the strips in each rolling turn. We first model this two-stage problem considering a set of production constraints and then design an optimal approach to solve the strip combination problem. Subsequently, we design an evolutionary algorithm (i.e., artificial bee colony algorithm) with a novel search strategy for employed bees, a dynamic strategy for onlooker bees, a variable neighborhood search strategy for a scout bee, and an enhanced strategy to solve the problem in the second stage. Computational experiments demonstrate the effectiveness of the proposed algorithms.Note to Practitioners—The hot rolling batch scheduling process is crucial in linking the casting and rolling processes of iron and steel productions. In the rolling batch scheduling problem of CSP, there is no buffer between the casting and rolling processes, and virtual strips must be added to satisfy production constraints. Most rolling batch scheduling methods do not consider the addition of virtual strips. In this paper, we mathematically characterize the hot rolling batch scheduling problem in CSP with flexible production constraints. We then show how the optimal approach and artificial bee colony algorithm are designed. Finally, the effectiveness of the proposed algorithms is demonstrated by comparisons with other well-known metaheuristic algorithms. This paper can be extended to other hot rolling batch scheduling problems with buffers and hybrid flowshop scheduling problems.

This paper proposes a novel penalty function measure for constrained optimization using a new harmony search algorithm. In the proposed algorithm, a two-stage penalty is applied to the infeasible solutions. In the first stage, the algorithm can search for feasible solutions with better objective values efficiently. In the second stage, the algorithm can take full advantage of the information contained in infeasible individuals and avoid trapping in local optimum. In addition, for adapting to this method, a new harmony search algorithm is presented, which can keep a balance between exploration and exploitation in the evolution process. Numerical results of 13 benchmark problems show that the proposed algorithm performs more effectively than the ordinary methods for constrained optimization problems.