Parallel machine scheduling with position-based deterioration and learning effects in an uncertain manufacturing system

Abstract

This research investigates a parallel machine scheduling problem with position-dependent deteriorating jobs and DeJong’s learning effects in an uncertain manufacturing system. We utilize the fuzzy set theory to handle such an uncertain system. The objective is to minimize the fuzzy makespan. To tackle such an NP-hard scheduling problem, we design a fuzzy simplified swarm optimization algorithm with local search, which is appraised by comparing it with other state-of-the-art meta-heuristics. Given that the parameter values of meta-heuristic algorithms have a remarkable impact on performance, we first apply the Taguchi method to select proper values for the control factors. Afterward, extensive numerical comparisons are conducted. Finally, to further analyze the performance of meta-heuristic algorithms from a statistical viewpoint, Friedman’s test and the Wilcoxon signed-rank test are carried out. Results imply that the designed algorithm can find satisfactory solutions in a reasonable time and performs better than the compared algorithms.