Workshop multi-object scheduling under fuzzy processing time and rewards-punishments grey target decision

To achieve multi-objective optimization scheduling of flexible workshops under fuzzy processing time conditions, an optimization method based on neighborhood dynamic selection NSGA-II algorithm and a decision method based on weighted grey target theory were proposed. A multi-objective optimization scheduling model was established using fuzzy set theory for the workshop scheduling problem under fuzzy processing time conditions. In terms of scheduling optimization, the NSGA-II algorithm selection strategy was improved, and a multi-objective optimization method for workshop scheduling using neighborhood dynamic selection NSGA-II was constructed. In terms of decision-making, the reward and punishment operator was introduced into the grey target decision-making theory, and this method could determine the optimal result in the sense of information entropy. Through production case verification, compared with the standard NSGA-II algorithm, chaotic mapping NSGA-II algorithm, and double-layer genetic algorithm, the Pareto solution set of the neighborhood dynamic selection NSGA-II algorithm is in a dominant position, indicating that the optimization ability of this method is the strongest. The optimal scheduling scheme determined by the weighted grey target theory satisfies both time and logical constraints, which means it is a feasible scheduling scheme. The experimental results indicate that the optimization and decision-making methods proposed in this paper are feasible and they also have certain advantages.