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Journal of Intelligent Manufacturing

Erschienen in:

27.03.2018

A scheduling optimization method for maintenance, repair and operations service resources of complex products

verfasst von: Hao Li, Shanghua Mi, Qifeng Li, Xiaoyu Wen, Dongping Qiao, Guofu Luo

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 7/2020

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Abstract

Scheduling optimization of maintenance, repair and operations (MRO) service resources of complex product can help an enterprise improve customer service satisfaction, build value-added products, and enhance the enterprise’s market competitiveness. This paper studies a scheduling optimization method for the MRO service resources of complex product. First, the scheduling problem in service resources is analyzed, a mathematical model for the service scheduling problem is given, and three objective functions to be optimized are proposed, which are to reduce customer waiting time, reduce excessive human resources, and maximize the cost performance index of the resources. Then, three separate objectives for optimizing service resource scheduling are analyzed based on three proposed methods which are improved genetic algorithm method, combined weight coefficient optimization method, and multi-objective optimization method based on the nondominated sorting genetic algorithm II (NSGA-II). Finally, we use the three methods to carry out scheduling optimization of MRO service resources in the case of a large vertical mill. According to the analysis and comparison of results, the multi-objective optimization method based on the NSGA-II algorithm has an advantage in the scheduling optimization of complex product MRO service resources. In the engineering application, the service scheduling of complex products for managers provides theoretical basis, and can reduce the loss caused by subjective judgment.

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Titel: A scheduling optimization method for maintenance, repair and operations service resources of complex products
verfasst von: Hao Li
Shanghua Mi
Qifeng Li
Xiaoyu Wen
Dongping Qiao
Guofu Luo
Publikationsdatum: 27.03.2018
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 7/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-018-1400-4

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