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Risk-averse hub location: Formulation and solution approach

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Risk-averse hub location: Formulation and solution approach. / Kargar, Kamyar; Mahmutoğulları, Ali İrfan.
In: Computers and Operations Research, Vol. 143, 105760, 31.07.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Kargar, K & Mahmutoğulları, Aİ 2022, 'Risk-averse hub location: Formulation and solution approach', Computers and Operations Research, vol. 143, 105760. https://doi.org/10.1016/j.cor.2022.105760

APA

Kargar, K., & Mahmutoğulları, A. İ. (2022). Risk-averse hub location: Formulation and solution approach. Computers and Operations Research, 143, Article 105760. https://doi.org/10.1016/j.cor.2022.105760

Vancouver

Kargar K, Mahmutoğulları Aİ. Risk-averse hub location: Formulation and solution approach. Computers and Operations Research. 2022 Jul 31;143:105760. Epub 2022 Mar 22. doi: 10.1016/j.cor.2022.105760

Author

Kargar, Kamyar ; Mahmutoğulları, Ali İrfan. / Risk-averse hub location : Formulation and solution approach. In: Computers and Operations Research. 2022 ; Vol. 143.

Bibtex

@article{2e31f063a596498da9e540a24011a306,
title = "Risk-averse hub location: Formulation and solution approach",
abstract = "In this study, we present risk-neutral and risk-averse two-stage stochastic formulations for the uncapacitated multiple allocation p-hub median problem and discuss the impact of risk-aversion on the optimal solution. Although stochastic models are useful to tackle the uncertainty in problem parameters, the solution of these models requires higher computational effort than their deterministic counterparts. Therefore, we present a scenario decomposition algorithm for the stochastic formulations. To evaluate the performance of the proposed solution algorithm, a set of computational experiments is conducted on real data sets. The results show that the proposed algorithm is very effective in finding optimal or near-optimal solutions in significantly shorter computation time than that of deterministic equivalent problems.",
keywords = "Hub location, Risk-averse optimization, Scenario decomposition, Stochastic programming",
author = "Kamyar Kargar and Mahmutoğulları, {Ali İrfan}",
note = "Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",
year = "2022",
month = jul,
day = "31",
doi = "10.1016/j.cor.2022.105760",
language = "English",
volume = "143",
journal = "Computers and Operations Research",
issn = "0305-0548",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Risk-averse hub location

T2 - Formulation and solution approach

AU - Kargar, Kamyar

AU - Mahmutoğulları, Ali İrfan

N1 - Publisher Copyright: © 2022 Elsevier Ltd

PY - 2022/7/31

Y1 - 2022/7/31

N2 - In this study, we present risk-neutral and risk-averse two-stage stochastic formulations for the uncapacitated multiple allocation p-hub median problem and discuss the impact of risk-aversion on the optimal solution. Although stochastic models are useful to tackle the uncertainty in problem parameters, the solution of these models requires higher computational effort than their deterministic counterparts. Therefore, we present a scenario decomposition algorithm for the stochastic formulations. To evaluate the performance of the proposed solution algorithm, a set of computational experiments is conducted on real data sets. The results show that the proposed algorithm is very effective in finding optimal or near-optimal solutions in significantly shorter computation time than that of deterministic equivalent problems.

AB - In this study, we present risk-neutral and risk-averse two-stage stochastic formulations for the uncapacitated multiple allocation p-hub median problem and discuss the impact of risk-aversion on the optimal solution. Although stochastic models are useful to tackle the uncertainty in problem parameters, the solution of these models requires higher computational effort than their deterministic counterparts. Therefore, we present a scenario decomposition algorithm for the stochastic formulations. To evaluate the performance of the proposed solution algorithm, a set of computational experiments is conducted on real data sets. The results show that the proposed algorithm is very effective in finding optimal or near-optimal solutions in significantly shorter computation time than that of deterministic equivalent problems.

KW - Hub location

KW - Risk-averse optimization

KW - Scenario decomposition

KW - Stochastic programming

U2 - 10.1016/j.cor.2022.105760

DO - 10.1016/j.cor.2022.105760

M3 - Journal article

AN - SCOPUS:85126701806

VL - 143

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

M1 - 105760

ER -