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Journal of Intelligent Manufacturing

Erschienen in:

25.06.2022

The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm

verfasst von: Wenkang Zhang, Yufan Zheng, Rafiq Ahmad

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 7/2023

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Abstract

This study considers an integrated process planning and scheduling (IPPS) problem for remanufacturing systems incorporating parallel disassembly workstations, a flexible job-shop-type reprocessing shop, and parallel reassembly workstations. This IPPS problem aims to determine the allocation/sequence of end-of-life products on the disassembly/reassembly shops and make decisions on the process path selection, operation sequencing, workstation allocation, and selection for reprocessing jobs. To solve the problem, a mixed-integer programming model is first built to characterize it mathematically, and a novel extended network graph is designed to represent and solve this problem visually. Then, an improved artificial bee colony algorithm is proposed that can solve the IPPS problem of remanufacturing systems with disassembly, reworking and reassembly shops simultaneously. In this introduced algorithm, a 3-level real-number solution representation scheme is adopted for encoding and decoding processes, and efficient neighborhood search structures are designed to improve the quality and diversity of the population. Computational experiments were systematically conducted on serval test instances. The results show that the proposed algorithm is highly advantageous for solving the IPPS problems in the remanufacturing systems by comparing it with four baseline algorithms.

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Titel: The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm
verfasst von: Wenkang Zhang
Yufan Zheng
Rafiq Ahmad
Publikationsdatum: 25.06.2022
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 7/2023
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-022-01969-2

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