Title: An improved whale optimisation algorithm for distributed assembly flow shop with crane transportation

Authors: Qing-Hua Li; Jun-Qing Li; Qing-Ke Zhang; Peng Duan; Tao Meng

Addresses: School of Information and Engineering, Shandong Normal University, Jinan 250014, China ' School of Information and Engineering, Shandong Normal University, Jinan 250014, China; School of Computer Science, Liaocheng University, Liaocheng 252059, China ' Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA ' School of Computer Science, Liaocheng University, Liaocheng 252059, China ' School of Mathematics, Liaocheng University, Liaocheng 252059, China

Abstract: In this study, we investigate a classical distributed assembly flow shop scheduling problem with crane transportation. The objectives are to minimise the weighted value of the makespan and the energy consumptions. An improved whale optimisation algorithm (IWOA) which is embedded with a simulated annealing (SA) algorithm is proposed to solve the considered problem. First, a clustering method is applied to divide the solutions to improve the performance of the algorithm. Then, a right shift heuristic is developed to reduce the number of machine switches, therefore decreasing the energy consumption. In addition, two novel crossover operators, namely, factory crossover and solution crossover, are designed to increase the overall performance of the proposed algorithm. Furthermore, a SA-based global search heuristic is embedded in the algorithm to enhance its exploration abilities. Finally, several real-world instances were generated to test the performance of the proposed algorithm. The experimental results show that this algorithm performs better than other comparable algorithms.

Keywords: distributed assembly flow shop scheduling; crane; energy consumptions; whale optimisation algorithm.

DOI: 10.1504/IJAAC.2021.118529

International Journal of Automation and Control, 2021 Vol.15 No.6, pp.710 - 743

Received: 01 Sep 2019
Accepted: 17 Mar 2020
Published online: 28 Oct 2021 *

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