Solving a Two-stage Three-machine Flowshop Assembly Scheduling Problem with Learning Consideration

Abstract

Recently, two-stage three-machine assembly flow shop issues have drawn a lot of researchers’ attention. However, the learning idea has not been widely introduced into two-stage assembly flow shop model. In view of this observation, authors then study a two-stage three-machine flow shop scheduling problem with learning consideration in which the goal is to minimize the total completion time of given n jobs. For saving time cost, authors proposed six types of particle swam optimization methods for searching approximate solutions compared to the lower bound for the small number of jobs and big numbers of jobs, respectively. Several statistical tools involving analysis of variance (ANOVA) and Fisher’s least significant difference tests are utilized to determine the proposed six PSOs for three data types in term of the average gap.