An efficient self-adaptive artificial bee colony algorithm for the distributed resource-constrained hybrid flowshop problem. (July 2022)

• Record Type: Journal Article
• Authors: Tao, Xin-Rui; Pan, Quan-Ke; Gao, Liang
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: Hybrid flowshop -- Artificial bee colony algorithm -- Resource-constrained -- Distributed -- Self-adaptive; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108200
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlight: It is the first time to considered the DRCHFS, and a mathematical model is proposed. A decoding heuristic strategy considering resource constraints is proposed. A self-adaptive strategy is utilized to generate efficient perturbation solutions. A local search strategy is designed. Abstract: Distributed factory processing has attracted the attention and application of many companies because of the low cost and high flexibility. In the present study, the self-adaptive artificial bee colony algorithm (SABC) is presented to solve the distributed resource-constrained hybrid flowshop scheduling (DRCHFS) problems aiming to minimize the makespan. In the proposed algorithm, the two-dimensional vector solution representation is employed. Then, resource constraint in the decoding process is considered. In addition, a self-adaptive perturbation structure and local search strategy based on the critical factory are investigated to enhance searching abilities. The proposed algorithm is tested based on a randomly generated set of the real shop scheduling system, and then numerically analyze and compare the proposed algorithm with the existing heuristic algorithms to verify its effectiveness.