Value-at-Risk-based fuzzy stochastic optimization problems

Shuming Wang; Junzo Watada

doi:10.1109/FUZZY.2009.5277422

Value-at-Risk-based fuzzy stochastic optimization problems

Shuming Wang^*, Junzo Watada

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

A new class of fuzzy stochastic optimization models - two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the twostage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Pages	1402-1407
Number of pages	6
DOIs	https://doi.org/10.1109/FUZZY.2009.5277422
Publication status	Published - 2009
Event	2009 IEEE International Conference on Fuzzy Systems - Jeju Island Duration: 2009 Aug 20 → 2009 Aug 24

Other

Other	2009 IEEE International Conference on Fuzzy Systems
City	Jeju Island
Period	09/8/20 → 09/8/24

Keywords

Fuzzy random variable
Fuzzy stochastic programming
Neural network
Particle swarm optimization
Value-at-Risk

ASJC Scopus subject areas

Software
Artificial Intelligence
Applied Mathematics
Theoretical Computer Science

Access to Document

10.1109/FUZZY.2009.5277422

Cite this

@inproceedings{ed99af37d6334994a93b780af41c114b,

title = "Value-at-Risk-based fuzzy stochastic optimization problems",

abstract = "A new class of fuzzy stochastic optimization models - two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the twostage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.",

keywords = "Fuzzy random variable, Fuzzy stochastic programming, Neural network, Particle swarm optimization, Value-at-Risk",

author = "Shuming Wang and Junzo Watada",

year = "2009",

doi = "10.1109/FUZZY.2009.5277422",

language = "English",

isbn = "9781424435975",

pages = "1402--1407",

booktitle = "IEEE International Conference on Fuzzy Systems",

note = "2009 IEEE International Conference on Fuzzy Systems ; Conference date: 20-08-2009 Through 24-08-2009",

}

TY - GEN

T1 - Value-at-Risk-based fuzzy stochastic optimization problems

AU - Wang, Shuming

AU - Watada, Junzo

PY - 2009

Y1 - 2009

N2 - A new class of fuzzy stochastic optimization models - two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the twostage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.

AB - A new class of fuzzy stochastic optimization models - two-stage fuzzy stochastic programming with Value-at-Risk (VaR) criteria is established in this paper. An approximation algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence theorem of the approximation algorithm is also proved. To solve the twostage fuzzy stochastic programming problems with VaR criteria, we integrate the approximation algorithm, neural network (NN) and particle swarm optimization (PSO) algorithm, and hence produce a hybrid PSO algorithm to search for the optimal solution. A numerical example is provided to illustrate the designed hybrid PSO algorithm.

KW - Fuzzy random variable

KW - Fuzzy stochastic programming

KW - Neural network

KW - Particle swarm optimization

KW - Value-at-Risk

UR - http://www.scopus.com/inward/record.url?scp=71249116612&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=71249116612&partnerID=8YFLogxK

U2 - 10.1109/FUZZY.2009.5277422

DO - 10.1109/FUZZY.2009.5277422

M3 - Conference contribution

AN - SCOPUS:71249116612

SN - 9781424435975

SP - 1402

EP - 1407

BT - IEEE International Conference on Fuzzy Systems

T2 - 2009 IEEE International Conference on Fuzzy Systems

Y2 - 20 August 2009 through 24 August 2009

ER -

Value-at-Risk-based fuzzy stochastic optimization problems

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this