Paper Info
Title
Value-at-Risk-Based Two-Stage Fuzzy Facility Location Problems
Abstract
Reducing risks in location decisions when coping with imprecise information is critical in supply chain management so as to increase competitiveness and profitability. In this paper, a two-stage fuzzy facility location problem with value-at-risk (VaR), called VaR-FFLP, is proposed, which results in a two-stage fuzzy zero-one integer programming problem. Some properties of the VaR-FFLP, including the value of perfect information (VPI), the value of fuzzy solution (VFS), and the bounds of the fuzzy solution, are discussed. Since the fuzzy parameters of the location problem are represented in the form of continuous fuzzy variables, the determination of VaR is inherently an infinite-dimensional optimization problem that cannot be solved analytically. Therefore, a method based on the discretization of the fuzzy variables is proposed to approximate the VaR. The approximation approach converts the original problem into a finite-dimensional optimization problem. A pertinent convergence theorem for the approximation approach is proved. Subsequently, by combining the simplex algorithm, the approximation approach, and a mechanism of genotype-phenotype-mutation-based binary particle swarm optimization (GPM-BPSO), a hybrid GPM-BPSO algorithm is being exploited to solve the VaR-FFLP. A numerical example illustrates the effectiveness of the hybrid GPM-BPSO algorithm and shows its enhanced performance in comparison with the results obtained by other approaches using genetic algorithm (GA), tabu search (TS), and Boolean BPSO (B-BPSO).
Year
DOI
Venue
2009
10.1109/TII.2009.2022542
IEEE Trans. Industrial Informatics
Keywords
Field
DocType
value-of-perfect information,binary particle swarm optimization (bpso),fuzzy set theory,pertinent convergence theorem,tabu search,discretization method,value-at-risk (var),approximate approach,supply chain management,two-stage fuzzy facility location problem,tabu search (ts),particle swarm optimisation,finite-dimensional optimization problem,profitability,integer programming,simplex algorithm,search problems,risk analysis,convergence,facility location,boolean bpso,genetic algorithm,genotype-phenotype-mutation-based binary particle swarm optimization,genetic algorithm (ga),genetic algorithms,infinite-dimensional optimization problem,fuzzy variable,zero-one integer programming problem,value-at-risk,approximation approach,value-of-fuzzy solution,optimization problem,supply chains,particle swarm optimization,reactive power,facility location problem,value at risk,approximation algorithms,linear programming,value of perfect information
Mathematical optimization,Fuzzy classification,Defuzzification,Computer science,Fuzzy set operations,Fuzzy logic,Fuzzy transportation,Fuzzy set,Fuzzy number,Optimization problem
Journal
Volume
Issue
ISSN
5
4
1551-3203
Citations 
PageRank 
References 
25
1.32
31
Authors
3
Name
Order
Citations
PageRank
Shuming Wang122915.96
Junzo Watada241184.53
W. Pedrycz3139661005.85