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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.

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.

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.