Open Access

ARTICLE

Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

Xiwang Guo¹, Liangbo Zhou¹, Zhiwei Zhang^1,*, Liang Qi^2,*, Jiacun Wang³, Shujin Qin⁴, Jinrui Cao⁵

1 College of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 113000, China
2 Department of Computer Science and Technology, Shandong University of Science and Technology, Qingdao, 266590, China
3 Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ 07764, USA
4 Research Center of the Economic and Social Development of Henan East Provincial Joint, Shangqiu Normal University, Shangqiu, 476000, China
5 Department of Computer Science, New Jersey City University, Jersey City, NJ 07305, USA

* Corresponding Authors: Zhiwei Zhang. Email: ; Liang Qi. Email:

(This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)

Computers, Materials & Continua 2024, 80(3), 4475-4496. https://doi.org/10.32604/cmc.2024.048123

Received 28 November 2023; Accepted 30 April 2024; Issue published 12 September 2024

Abstract

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers. A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time. Based on a product’s AND/OR graph, matrices for task-skill, worker-skill, precedence relationships, and disassembly correlations are developed. A multi-objective discrete chemical reaction optimization algorithm is designed. To enhance solution diversity, improvements are made to four reactions: decomposition, synthesis, intermolecular ineffective collision, and wall invalid collision reaction, completing the evolution of molecular individuals. The established model and improved algorithm are applied to ball pen, flashlight, washing machine, and radio combinations, respectively. Introducing a Collaborative Resource Allocation (CRA) strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm, the experimental results are compared with four classical algorithms: MOEA/D, MOEAD-CRA, Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Non-dominated Sorting Genetic Algorithm III (NSGA-III). This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

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Keywords

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