Hybrid Salp Swarm Algorithm for Solving the Green Scheduling Problem in a Double-Flexible Job Shop

• Published in: Applied sciences Vol. 12; no. 1; p. 205
• Main Authors: Liu, Changping, Yao, Yuanyuan, Zhu, Hongbo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Algorithms; double flexible job-shop scheduling; Energy consumption; Environmental impact; green shop scheduling; hybrid salp swarm algorithm; Job shops; Labor costs; Manufacturing; Mathematical programming; multi-objective optimization; Noise; Objectives; Optimization; Scheduling; Semiconductors; Workers
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12010205

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.