A scheduling optimization method for maintenance, repair and operations service resources of complex products
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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.
KeywordsMaintenance, repair and operations Scheduling optimization Improved genetic algorithm NSGA-II algorithm
The authors are grateful to the anonymous reviewers for their comments, which have helped to improve this paper.
This work was supported by the National Natural Science Foundation of China (No. 51775517), the National Science and Technology Support Plan Project of China (No. 2015BAF32B04), the Plan for Scientific Innovation Talent of Henan Province (No. 184100510007), the University Key Teacher of Henan Province (No. 2014GGJS-083), and the Research Fund for the Doctoral Program of Zhengzhou University of Light Industry (No. 2013BSJJ032).
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Conflict of interest
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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