Abstract
Aiming at the hybrid flow shop scheduling problem (HFSP), a multi-objective optimization scheduling model is established with the objectives of minimizing makespan, energy consumption and total load, and an improved non-dominated sorting genetic algorithm-II (INSGA-II) is proposed to solve the problem. Combining the problem characteristics of task sequencing and equipment selection in the production process of hybrid flow shop, a double-layer coding rule combining the job sequence code (JSC) and machine allocation code (MAC) is adopted. Different crossover and mutation operators are used for JSC and MAC respectively to improve the global search capability of the algorithm. Aiming at the poor local search capability of NSGA-II, neighborhood search is introduced to improve the quality of population. Finally, the optimal parameters combination of the algorithm is determined by orthogonal experiment, and the feasibility and effectiveness of INSGA-II for solving the multi-objective scheduling problem in hybrid flow shop are verified by simulation experiments.
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