Affiliations: [a] School of Management, Hefei University of Technology, Hefei, China
| [b] Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education, Hefei, China
| [c] Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States
Correspondence:
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Corresponding author. Shaojun Lu, School of Management, Hefei University of Technology, Hefei, China. E-mail: [email protected].
Note: [1] This work was supported by the National Key Research and Development Program of China (2019YFB1705300), the National Natural Science Foundation of China (Nos. 72101071, 72071056, 72101077, 72271077, 71922009), Key Research and Development Project of Anhui Province (2022a05020023), Natural Science Foundation of Anhui Province (2108085QG287, 2008085QG341, 1908085MG223), the Fundamental Research Funds for the Central Universities (Nos. JZ2021HGTA0134, JZ2021HGQA0200, JZ2022HGTB0356), Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product (111 project: B17014), the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making, Hefei University of Technology and the Ministry of Education.
Abstract: The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved Moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm.
