Title: Solving flexible job shop scheduling using an effective memetic algorithm

Authors: Wenchao Yi; Xinyu Li; Baolin Pan

Addresses: State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan 430074, China ' State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan 430074, China ' Research Institute of Nuclear Power Operation, Wuhan 430223, China

Abstract: This paper proposes an effective Memetic Algorithm (MA) for the Flexible Job Shop Scheduling Problem (FJSP) with the objective of minimising the makespan. The proposed MA is a combination of TABU Search (TS) and Genetic Algorithm (GA). This hybridisation presents an effective way of performing both exploration and exploitation by incorporating the local search abilities of TS with the global reaching capabilities of GA. The approach provides an effective encoding method, genetic operators and neighbourhood structure in order to effectively solve the FJSP. To evaluate the performance of the proposed MA, several benchmark instances of FJSP have been used. The experimental results show that the proposed MA is a very effective method for solving FJSP.

Keywords: memetic algorithms; FJSP; flexible job shop scheduling problem; makespan minimisation; tabu search; genetic algorithms; exploration; exploitation; local search; global reaching.

DOI: 10.1504/IJCAT.2016.074454

International Journal of Computer Applications in Technology, 2016 Vol.53 No.2, pp.157 - 163

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 16 Jan 2016 *

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