Multi-Objective Memetic Search Algorithm for Multi-Objective Permutation Flow Shop Scheduling Problem

The multiobjective permutation flowshop scheduling problem (MOPFSSP) is one of the most popular machine scheduling problems with extensive engineering relevance of manufacturing systems. There have been many attempts at solving MOPFSSP using heuristic and meta-heuristic methods, such as evolutionary algorithm. In this paper, a novel multiobjective memetic search algorithm (MMSA), is proposed to solve the MOPFSSP with makespan and total flowtime. First, a problem-specific Nawaz-Enscore-Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a global search embedded with a perturbation operation is used to improve the solution of the entire population. Then, a single insert-based local search is used to improve each individual and then a further local search strategy is used to find the better solution for the non-improved individual in the single insert-based local search. The performance of our proposed algorithm is validated and compared with the four state-of-the-art algorithms on a number of benchmark problem. The experimental results show that the proposed MMSA provides better solutions than several state-of-the-art algorithms.