In EDS ansehen

Distributed maintenance task scheduling for multiple technician teams considering uncertain durations and deterioration effects towards Industry 5.0.

Gespeichert in:
Bibliographische Detailangaben
Titel: Distributed maintenance task scheduling for multiple technician teams considering uncertain durations and deterioration effects towards Industry 5.0.
Autoren: Lu, Shaojun1,2 (AUTHOR) lushaojun@hfut.edu.cn, Chen, Yubei1 (AUTHOR), Zheng, Rui1,2 (AUTHOR) rui_zheng@hfut.edu.cn, Yan, Binxiao1,2 (AUTHOR), Liu, Xinbao1,2 (AUTHOR)
Quelle: International Journal of Production Research. Feb2025, p1-25. 25p. 11 Illustrations.
Schlagwörter: *MANUFACTURING processes, *PROBABILITY theory, *SCHEDULING, NP-hard problems, TIME measurements
Abstract: Industry 5.0 emphasises a return to human-centricity to ensure outcomes for humans in the manufacturing system and create resilient and sustainable systems. This study explores the broader challenge of optimising maintenance scheduling in complex and dynamic industrial environments, focusing on developing human-centric and resilient approaches for enterprises under Industry 5.0. The combined impact of uncertain task durations and time-dependent deterioration effects are taken into consideration. The objective is to improve the system's robustness, quantified by multiplying completion probabilities. We propose Algorithm-1, which leverages the structural properties derived from analyzing the single maintenance team problem. Based on the structural properties of the multi-team scenario, we also propose Algorithm-2 to improve task assignment. Given the NP-hard nature of the problem, we further propose an improved variable neighbourhood search (IVNS), incorporating three local search methodologies and proposed algorithms to achieve near-optimal solutions within a reasonable timeframe. Comprehensive computational experiments on diverse problem instances indicate that the proposed IVNS algorithm outperforms other approaches, demonstrating significant robustness and efficiency. Specifically, across 36 experimental instances, IVNS achieved an average improvement of 39.7% relative to the lower bound, indicating a substantial enhancement in system-wide task completion probabilities. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Business Source Index
Beschreibung
Abstract:Industry 5.0 emphasises a return to human-centricity to ensure outcomes for humans in the manufacturing system and create resilient and sustainable systems. This study explores the broader challenge of optimising maintenance scheduling in complex and dynamic industrial environments, focusing on developing human-centric and resilient approaches for enterprises under Industry 5.0. The combined impact of uncertain task durations and time-dependent deterioration effects are taken into consideration. The objective is to improve the system's robustness, quantified by multiplying completion probabilities. We propose Algorithm-1, which leverages the structural properties derived from analyzing the single maintenance team problem. Based on the structural properties of the multi-team scenario, we also propose Algorithm-2 to improve task assignment. Given the NP-hard nature of the problem, we further propose an improved variable neighbourhood search (IVNS), incorporating three local search methodologies and proposed algorithms to achieve near-optimal solutions within a reasonable timeframe. Comprehensive computational experiments on diverse problem instances indicate that the proposed IVNS algorithm outperforms other approaches, demonstrating significant robustness and efficiency. Specifically, across 36 experimental instances, IVNS achieved an average improvement of 39.7% relative to the lower bound, indicating a substantial enhancement in system-wide task completion probabilities. [ABSTRACT FROM AUTHOR]
ISSN:00207543
DOI:10.1080/00207543.2025.2461146