In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated.

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.

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.

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.

As environmental awareness grows, energy-aware scheduling is attracting increasing attention. Compared with traditional flexible job shop scheduling problem (FJSP), FJSP, with considering sequence-dependent setup times and transportation times (FJSP-SDST-T), is closer to real production. In existing research, little research has focused on FJSP-SDST-T with the minimization energy consumption. In order to make up the gap, a mixed integer linear programming (MILP) model has been formulated to solve FJSP-SDST-T with minimizing energy. Firstly, the total energy consumption of the workshop included the processing energy consumption, setup energy consumption, idle energy consumption, transportation energy consumption and common energy consumption, which were analyzed and formulated by introducing related decision variables. Then, the MILP model was detailedly formulated from the formulation of the energy consumption composition, the objective function, the decision variables and the constraint sets and the linearization. Finally, experiments were carried out on extended benchmark cases and the results showed the effectiveness of the MILP model.

Energy conservation, emission reduction, and green and low carbon are of great significance to sustainable development, and are also the theme of the transformation and upgrading of the manufacturing industry. This paper concentrates on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. To solve the problem, the HFSP_ECS is decomposed by the idea of “divide-and-conquer”, resulting in three coupled subproblems, i.e., lot sequence, machine assignment, and lot split, which can be solved by using a cooperative methodology. Thus, an improved cooperative coevolutionary algorithm (vCCEA) is proposed by integrating the variable neighborhood descent (VND) strategy. In the vCCEA, considering the problem-specific characteristics, a two-layer encoding strategy is designed to represent the essential information, and a novel collaborative model is proposed to realize the interaction between subproblems. In addition, special neighborhood structures are designed for different subproblems, and two kinds of enhanced neighborhood structures are proposed to search for potential promising solutions. A collaborative population restart mechanism is established to ensure the population diversity. The computational results show that vCCEA can coordinate and solve each subproblem of HFSP_ECS effectively, and outperform the mathematical programming and the other state-of-the-art algorithms.