Study On Multi-objective Permutation Flow Shop Scheduling Problem With Improved Food-chain Algorithm

With the economic globalization, technological advances, production scheduling has become a big challenge for companies to maintain status in a rapidly changing market.Companies need to constantly improve their scheduling capabilities in order to meet the fierce market competition. Therefore, the scheduling problem has been a research focus.Flow shop scheduling has wide application across manufacturing industry.Permutation flow shop scheduling problem, as the simplified model of many assembly line production scheduling, has a good representative and that is an important object of study. In the real world, scheduling decisions in many industries may involve multiple targets which even contain restrictions and conflicts among them.How to solve the multi-objective flow shop scheduling problem decisions, so as to meet the production requirements, improve customer satisfaction, to achieve a win-win, all these problems have been gradually received scholars’ attention. Therefore, this paper determined to study the multi-objective permutation flow shop scheduling problem under both certainty and fuzzy conditions.Under certainty, the multi-objective permutation flow shop scheduling problem is described. Since the superiority of the food-chain algorithm which can effectively solve the flow shop problems, an improved food-chain algorithm is proposed. The algorithm ensures the diversity of the Pareto optimal solutions by introducing fast non-dominated sorting and crowding distance to the food-chain algorithm. The strategy of keeping elitism and variable Pareto local search improve the efficiency. Typical flow shop optimization problems are re-optimized by NSGA-II and the new algorithm respectively. The comparison of the results has shown the effectiveness and efficiency of the improved food-chain algorithm.Under fuzzy conditions, because of the uncertainty of scheduling parameters, fuzzy numbers are introduced to represent the relevant parameters. First, the fuzzy multi-objective permutation flow shop scheduling problem is described, and fuzzy multi-objective scheduling model is established. In the scheduling model, one of objective functions uses area compensation method for defuzzification, and the other function is established based on the pessimistic criterion in possibility theory. The satisfaction model introduces the satisfaction at a high plausibility point to the pessimistic criterion, so decision-makers can not only take into account the preferences, but also be more consistent with the actual situations. Finally, typical fuzzy flow shop optimization problems are re-optimized by the fuzzy multi-objective scheduling model. The results have shown the effectiveness and efficiency of the model.