Multi-objective scheduling for surface mount technology workshop: automatic design of two-layer decomposition-based approach

In the realm of cellular manufacturing systems (CMS), the scenarios where cells are organised as flowlines have gained substantial practical prevalence. Our focus centres on the domain of the Surface Mount Technology (SMT) workshop, a classical CMS, where cells are harmoniously coordinated to handle...

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Bibliographic Details
Published in:International journal of production research Vol. 63; no. 20; pp. 7570 - 7590
Main Authors: Zhang, Biao, Wang, Zhi-xuan, Meng, Lei-lei, Sang, Hong-yan, Jiang, Xu-chu
Format: Journal Article
Language:English
Published: London Taylor & Francis 18.10.2025
Taylor & Francis LLC
Subjects:
Algorithms
automated algorithm design
Cellular manufacture
Decomposition
Distributed flowshop scheduling
group scheduling
heuristic
Integer programming
Mixed integer
multi-objective optimisation
Multiple objective analysis
Scheduling
Surface mount technology
Workshops
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary:In the realm of cellular manufacturing systems (CMS), the scenarios where cells are organised as flowlines have gained substantial practical prevalence. Our focus centres on the domain of the Surface Mount Technology (SMT) workshop, a classical CMS, where cells are harmoniously coordinated to handle intricate production tasks. Two cell-based objectives with a trade-off relationship, namely the number of enabled cells and the makespan among the enabled cells, are introduced. The resulting scheduling problem is referred to as multi-objective reconfigurable distributed flowshop group scheduling problem (MORDFGSP). To tackle this problem, a multi-objective mixed integer programming model is proposed as an analytical tool. Recognising the NP-hard nature of the problem, we develop a two-layer decomposition-based approach that integrates the decomposition-based constructive and improvement heuristics. These heuristics can be configured adopting optional operators for various algorithm components. Within the framework of the developed approach, the automated algorithm design (AAD) is employed to conceive an automated multi-objective algorithm (AMOA) with minimal manual intervention. In the experimental study, the effectiveness of various algorithm components within the approach is thoroughly verified. Furthermore, comparative analyses with alternative methodologies provide strong evidence of the significant superiority of the AMOA.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2025.2502106