Paper Info
Title
A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty
Abstract
This paper studies a facility location model with fuzzy random parameters and its swarm intelligence approach. A Value-at-Risk (VaR) based fuzzy random facility location model (VaR-FRFLM) is built in which both the costs and demands are assumed to be fuzzy random variables, and the capacity of each facility is unfixed but a decision variable assuming continuous values. Under this setting, the VaR-FRFLM is inherently a two-stage mixed 0-1 integer fuzzy random programming problem, to which analytical nonlinear programming methods are not applicable. A hybrid modified particle swarm optimization (MPSO) approach is proposed to solve the VaR-FRFLM. In this hybrid mechanism, an approximation algorithm is utilized to compute the fuzzy random VaR objective, a continuous Nbest-Gbest-based PSO and a genotype-phenotype-based binary PSO vehicles are designed to deal with the continuous capacity decisions and the binary location decisions, respectively, and two mutation operators are incorporated into the PSO to further decrease the possibility of becoming trapped in the local optima. A numerical experiment illustrates the application of the proposed hybrid MPSO algorithm and lays out its robustness to the system parameter settings. The comparison shows that the hybrid MPSO exhibits better performance than that when hybrid regular continuous-binary PSO and genetic algorithm (GA) are used to solve the VaR-FRFLM.
Year
DOI
Venue
2012
10.1016/j.ins.2010.02.014
Information Sciences: an International Journal
Keywords
Field
DocType
hybrid modified particle swarm,hybrid mechanism,fuzzy random parameter,hybrid regular continuous-binary,hybrid mpso,variable capacity,integer fuzzy random programming,fuzzy random variable,proposed hybrid mpso algorithm,var-based facility location problem,fuzzy random uncertainty,hybrid modified pso approach,fuzzy random facility location,fuzzy random var objective,facility location,genetic algorithm,facility location problem,nonlinear programming,value at risk,particle swarm optimization,swarm intelligence
Particle swarm optimization,Approximation algorithm,Mathematical optimization,Fuzzy logic,Nonlinear programming,Swarm intelligence,Multi-swarm optimization,Facility location problem,Artificial intelligence,Machine learning,Genetic algorithm,Mathematics
Journal
Volume
ISSN
Citations 
192,
0020-0255
25
PageRank 
References 
Authors
0.92
21
2
Name
Order
Citations
PageRank
Shuming Wang122915.96
Junzo Watada241184.53