Abstract
Maintenance planning plays a key role in equipment operational management, and strategic equipment maintenance planning (SEML) is an integrated and complicated optimization problem consisting of more than one objectives and constraints. In this paper we present a new multi-objective particle swarm optimization (PSO) algorithm for effectively solving the SEML problem model whose objectives include minimizing maintenance cost and maximizing expected mission capability of military equipment systems. Our algorithm employs an objective leverage function for global best selection, and preserves the diversity of non-dominated solutions based on the measurement of minimum pairwise distance. Experimental results show that our approach can achieve good solution quality with low computational costs to support effective decision-making.
This work was supported in part by grants from National Natural Science Foundation (No. 60773054, 61020106009, 90718036) of China.
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Ling, H., Zheng, Y., Zhang, Z., Zhou, X. (2011). A New Multi-Objective Particle Swarm Optimization Algorithm for Strategic Planning of Equipment Maintenance. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_8
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