A novel fuzzy model for multi-objective permutation flow shop scheduling problem with fuzzy processing time

This article proposes a novel fuzzy model for solving fuzzy multi-objective permutation flow shop scheduling problem with fuzzy processing time. Specifically, two fuzzy objectives, that is, the fuzzy makespan and the fuzzy total flow time, are taken into account in this model simultaneously. In addition, to solve fuzzy multi-objective permutation flow shop scheduling problem, an efficient algorithm called fuzzy multi-objective local search-based decomposition is proposed. In order to generate a high quality and diverse set of initial solutions, a problem-specific Nawaz-Enscore-Ham heuristic approach is incorporated into the framework as an initialization. Then, two perturbation strategies with different strength are adopted to find better solutions and to avoid local optimum as well. Besides, a ranking concept based on fuzzy number centroid is provided to compare with fuzzy solutions. After that, a restart strategy is employed to change the searching space when the best solution has not been improved for a certain number of iterations. Finally, we conduct an extensive computational study on Taillard benchmarks to compare the proposed fuzzy multi-objective local search-based decomposition with the fuzzy NSGAII. Experimental results demonstrate that the fuzzy multi-objective local search-based decomposition algorithm is both effective and efficient in solving the fuzzy multi-objective permutation flow shop scheduling problem.