Paper Info
Title
A new multi-objective particle swarm optimization algorithm for strategic planning of equipment maintenance
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.
Year
DOI
Venue
2011
10.1007/978-3-642-21524-7_8
ICSI
Keywords
Field
DocType
complicated optimization problem,optimization algorithm,strategic equipment maintenance planning,maintenance planning,maintenance cost,new multi-objective particle swarm,effective decision-making,strategic planning,global best selection,seml problem model,military equipment system,equipment operational management
Particle swarm optimization,Pairwise comparison,Mathematical optimization,Leverage (finance),Computer science,Algorithm,Multi-objective optimization,Maintenance planning,Strategic planning,Optimization problem
Conference
Volume
ISSN
Citations 
6729
0302-9743
0
PageRank 
References 
Authors
0.34
9
4
Name
Order
Citations
PageRank
Haifeng Ling121010.52
Yujun Zheng237628.53
Ziqiu Zhang300.34
Xianzhong Zhou443927.01