Title: Swarm intelligent algorithm for re-entrant hybrid flow shop scheduling problems

Authors: Zhonghua Han; Xutian Tian; Xiaoting Dong; Fanyi Xie

Addresses: Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China; Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China ' Faculty of Electrical Engineering, Sichuan College of Architectural Technology, Deyang, Sichuan, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China

Abstract: In order to solve re-entrant hybrid flowshop (RHFS) scheduling problems and establish simulations and processing models, this paper uses wolf pack algorithm (WPA) as global optimisation. For local assignment, it takes minimum remaining time rule. Scouting behaviours of wolf are changed in former optimisation by means of Levy flight, extending searching ranges and increasing rapidity of convergence. When it comes to local extremum of WPA, dynamic regenerating individuals with high similarity adds diversity. Hamming distance is used to judge individual similarity for increased quality of individuals, enhanced search performance of the algorithm in solution space and promoted evolutionary vitality. A painting workshop in a bus manufacture enterprise owns typical features of re-entrant hybrid flowshop. Regarding it as the algorithm applied target, this paper focuses on resolving this problem with dynamic wolf pack algorithm based on levy flight (LDWPA). Results show that LDWPA can solve re-entrant hybrid flowshop scheduling problems effectively.

Keywords: re-entrant hybrid flow shop; RHFS; mathematics scheduling models; Hamming distance; Levy flight; swarm intelligent algorithm.

DOI: 10.1504/IJSPM.2019.097704

International Journal of Simulation and Process Modelling, 2019 Vol.14 No.1, pp.17 - 27

Received: 12 Jan 2018
Accepted: 05 Jun 2018
Published online: 05 Feb 2019 *

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