Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration

Chin-Chia Wu*,Jianyou Xu,Junjie Wu,Shuenn-Ren Cheng,Win-Chin Lin,Zi-Ling Wang

doi:10.1080/18756891.2016.1256572

Abstract

AbstractRecently, both the learning effect scheduling and re-entrant scheduling have received more attention separately in research community. However, the learning effect concept has not been introduced into re-entrant scheduling in the environment setting. To fill this research gap, we investigate re-entrant permutation flowshop scheduling with a position-based learning effect to minimize the total completion time. Because the same problem without learning or re-entrant has been proved NP-hard, we thus develop some heuristics and a genetic algorithm (GA) to search for approximate solutions. To solve this problem, we first adopt four existed heuristics for the problem; we then apply the same four methods combined with three local searches to solve the proposed problem; in the last stage we develop a heuristic-based genetic algorithm seeded with four good different initials obtained from the second stage for finding a good quality of solutions. Finally, we conduct experimental tests to evaluate the behavi...

Highlights

Most of classical scheduling models consider that the job processing time is fixed and constant [1,2,3]
Wu et al 35 considered the re-entrant permutation flowshop scheduling problem with a sumof-processing-times-based learning function to minimize the makespan. They developed four heuristics by combining Johnson’s rule with four local search methods that are effective in treating the flowshop scheduling problem and a simulated annealing (SA) algorithm to find near-optimal solutions for the problem. In view of these observations, in this paper we study the re-entrant permutation flowshop scheduling problem with a position-based learning function to minimize the total completion time which is a criterion popularly considered in the field of scheduling
The main goals of this study is that in the first stage we first adopt four existed heuristics for this problem; in the second stage we use the same four methods combined with three local searches to solve the proposed problem; and in the last stage we develop a heuristic-based genetic algorithm seeded with four good different initials obtained from the second stage to find near-optimal solutions

Summary

Introduction

Most of classical scheduling models consider that the job processing time is fixed and constant [1,2,3]. Wu et al 35 considered the re-entrant permutation flowshop scheduling problem with a sumof-processing-times-based learning function to minimize the makespan They developed four heuristics by combining Johnson’s rule with four local search methods that are effective in treating the flowshop scheduling problem and a simulated annealing (SA) algorithm to find near-optimal solutions for the problem. In view of these observations, in this paper we study the re-entrant permutation flowshop scheduling problem with a position-based learning function to minimize the total completion time which is a criterion popularly considered in the field of scheduling. The completion times of three jobs on machine M2 at level one are given as follows

Problem definition and description

The four algorithms combined with three local searches

Four heuristic-based genetic algorithms on the larger number of job

Findings

Conclusions