Title: Research on limited buffer scheduling problems in flexible flow shops with setup times

Authors: Zhonghua Han; Quan Zhang; Haibo Shi; Yuanwei Qi; Liangliang Sun

Addresses: Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China ' Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China

Abstract: In order to solve the limited buffer scheduling problem in flexible flow shops with setup times, an improved whale optimisation algorithm (IWOA) is proposed. Based on the whale optimisation algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimisation ability of WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and WOA algorithm are used for comparison. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under various practical evaluation scenarios. The results show that the IWOA algorithm can better solve this type of scheduling problems.

Keywords: limited buffer; improved whale optimisation algorithm; IWOA; levy flight; opposition-based learning strategy; simulated annealing; flexible flow shop.

DOI: 10.1504/IJMIC.2019.102360

International Journal of Modelling, Identification and Control, 2019 Vol.32 No.2, pp.93 - 104

Received: 19 Sep 2018
Accepted: 26 Nov 2018
Published online: 23 Sep 2019 *

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