Fleet Maintenance Strategy Planning with Time Windows Integrated with Multi-Agent and Wolf Pack Reinforcement Learning. Issue 4 (January 2021)

• Record Type: Journal Article
• Authors: Xinrui, Ma; Haixu, Li; Zheng, Zhou; Bo, Chen
• Is Part Of: IOP conference series. Volume 1043:Issue 4(2021)
• Publication Date: 2021-01
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1043/4/042036
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Selective maintenance is a widely used strategy to identify and perform the maintenance actions necessary for fleet mission success. Aiming at the problem of maintenance strategy planning with time windows (MSPTW) which is common in short-term operation plan, a fleet maintenance strategy planning approach based on multi-agent and reinforcement learning is studied in this paper. Based on the four kinds of foraging behaviors including migration, summon and attack in traditional Wolf Pack Algorithm (WPA), the intelligent behavior is redefined, and a new wolf pack algorithm for solving the MSPTW is designed. In order to seek the best path planning, a mathematical model with the aim of minimizing the total cost (fixed cost, transportation cost, waiting cost and penalty cost) is constructed utilizing the fitness and penalty function.