{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:04:45Z","timestamp":1777705485565,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,3,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/jifs-221994","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T11:34:49Z","timestamp":1672140889000},"page":"5275-5294","source":"Crossref","is-referenced-by-count":4,"title":["Preemptive multi-skill resource-constrained project scheduling of marine power equipment maintenance tasks1"],"prefix":"10.1177","volume":"44","author":[{"given":"Peng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaojun","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education, Hefei, China"},{"name":"Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei, China"},{"name":"Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education, Hefei, 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