Integrated GA and AHP for re-entrant flow shop scheduling problem

Abstract

This paper proposes a novel way to incorporate the analytical hierarchy analysis into the genetic algorithm to solve the flow shop scheduling problem with reentrant jobs. The proposed approach allows the manufacturers take many criteria into consideration genetic algorithm gets the near-optimal sequence while the analytical hierarchy analysis assists to fulfill the multiple criteria as well as fasten the convergence that nested in the selection procedure. Initial population given by the genetic algorithm is filtered by the AHP so that the preferred chromosomes are kept as parents to generate the offspring. To demonstrate how the proposed approach works for the re-entrant flow shop scheduling, a case study of a repairing company whose jobs with dynamic re-entrant characteristic have been conducted. The experiments simulate the case scenario and the results indicate the superiority of proposed method over the practical approach. This finding is able to provide a solid foundation on which the scheduler can enhance the efficiency and accuracy of the re-entrant scheduling.