Abstract:
A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artif...Show MoreMetadata
Abstract:
A permutation flow-shop scheduling problem (PFSP) has been studied for a long time due to its significance in real-life applications. This work proposes an improved artificial bee colony (ABC) algorithm with Q -learning, named QABC, for solving it with minimizing the maximum completion time (makespan). First, the Nawaz–Enscore–Ham (NEH) heuristic is employed to initialize the population of ABC. Second, a set of problem-specific and knowledge-based neighborhood structures are designed in the employ bee phase. Q -learning is employed to favorably choose the premium neighborhood structures. Next, an all-round search strategy is proposed to further enhance the quality of individuals in the onlooker bee phase. Moreover, an insert-based method is applied to avoid local optima. Finally, QABC is used to solve 151 well-known benchmark instances. Its performance is verified by comparing it with the state-of-the-art algorithms. Experimental and statistical results demonstrate its superiority over its peers in solving the concerned problems.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 53, Issue: 5, May 2023)