Modified self-adaptive local search algorithm for a biobjective permutation flow shop scheduling problem

Abstract

Interest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized.Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.PFSS problems are NP-hard problems even for a single objective and that is why there are numerous stateof-the-art metaheuristic algorithms for the approximate solutions of the problem. Referring interested readers to the comprehensive reviews of [3] and [4], Section 2 covers the studies that presented metaheuristic algorithms for biobjective PFSS problems. The metaheuristic algorithms given in Section 2 are all managed by a set of parameters, which has a significant impact on the solution quality and/or computational time. Searching for an * Correspondence: cigdem.uslu@marmara.edu.tr This work is licensed under a Creative Commons Attribution 4.0 International License.