Abstract
Green scheduling is not only an effective way to achieve green manufacturing but also an effective way for modern manufacturing enterprises to achieve energy conservation and emission reduction. The double-flexible job shop scheduling problem (DFJSP) considers both machine flexibility and worker flexibility, so it is more suitable for practical production. First, a multi-objective mixed-integer programming model for the DFJSP with the objectives of optimizing the makespan, total worker costs, and total influence of the green production indicators is formulated. Considering the characteristics of the problem, three-layer salp individual encoding and decoding methods are designed for the multi-objective hybrid salp swarm algorithm (MHSSA), which is hybridized with the Lévy flight, the random probability crossover operator, and the mutation operator. In addition, the influence of the parameter setting on the MHSSA in solving the DFJSP is investigated by means of the Taguchi method of design of experiments. The simulation results for benchmark instances show that the MHSSA can effectively solve the proposed problem and is significantly better than the MSSA and the MOPSO algorithm in the diversity, convergence, and dominance of the Pareto frontier.
Highlights
Traditional job shop scheduling problems usually do not consider human factors
It can be seen from the IGD and Ω indexes in Table 4 that the diversity, convergence, and dominance of the non-inferior solutions on the Pareto frontier obtained by the multi-objective hybrid salp swarm algorithm (MHSSA) are the best in the comparison algorithms and that the solution effects of the other two algorithms are not very different
The specific research contents include the following: (1) According to the characteristics of the doubleflexible job shop scheduling problem (DFJSP), a three-layer individual coding and decoding method of salp is designed, and it is decoded into an active scheduling scheme
Summary
Traditional job shop scheduling problems usually do not consider human factors. With increasing research on job shop scheduling problems, researchers believe that human factors should be considered in production scheduling and operation management activities [1,2,3,4]. To solve the flexible job shop scheduling problem, Lei et al [18] proposed a hybrid frog-leaping algorithm that considers the energy consumption index. Wu et al [19] took completion time and energy consumption as the optimization objectives, studied the flexible job shop problem with multi-speed machines, analyzed the energy consumption distribution of machines, established the energy consumption calculation model, and solved it by the NSGA-II algorithm. A double-flexible job shop green scheduling problem model is constructed with the objectives of minimizing the maximum completion time, the total labor cost, and the total green index. The double-flexible job shop green scheduling problem constructed in this paper has three optimization objectives: minimizing the maximum completion time, total labor cost, and total green index. The processing time, the labor cost, and the energy consumption corresponding to each process, as well as the attribute values of various green indexes of the machine, are known and fixed
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