Sciact
  • EN
  • RU

Upper Bound for the Competitive Facility Location Problem with Demand Uncertainty Full article

Journal Doklady Mathematics
ISSN: 1064-5624 , E-ISSN: 1531-8362
Output data Year: 2023, Volume: 108, Number: 3, Pages: 438-442 Pages count : 5 DOI: 10.1134/s1064562423600318
Tags bilevel programming, Stackelberg game, competitive facility location, pessimistic optimal solution
Authors Beresnev V.L. 1,2 , Melnikov A.A. 1,2
Affiliations
1 Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
2 Novosibirsk State University

Funding (1)

1 Russian Science Foundation 21-41-09017

Abstract: We consider a competitive facility location problem with two competing parties operating in a situation of uncertain demand scenarios. The problem of finding the best solutions for the parties is formulated as a discrete bilevel mathematical programming problem. A procedure for computing an upper bound for the objective function on solution subsets is suggested. The procedure could be employed in implicit enumeration schemes capable of computing an optimal solution for the problem under study. Within the procedure, additional constraints (cuts) iteratively augment the high-point relaxation of the initial bilevel problem, which strengthens the relaxation and improves the upper bound’s quality. A new procedure for generating such cuts is proposed, which allows us to construct the strongest cuts without enumerating the parameters encoding them.
Cite: Beresnev V.L. , Melnikov A.A.
Upper Bound for the Competitive Facility Location Problem with Demand Uncertainty
Doklady Mathematics. 2023. V.108. N3. P.438-442. DOI: 10.1134/s1064562423600318 WOS Scopus РИНЦ OpenAlex
Original: Береснев В.Л. , Мельников А.А.
Алгоритм вычисления верхних границ для задачи конкурентного размещения в условиях неопределенности спроса
Доклады Академии наук. Серия: Математика, информатика, процессы управления. 2023. Т.514. №1. С.20-25. DOI: 10.31857/S2686954323700327 РИНЦ OpenAlex
Dates:
Submitted: Apr 6, 2023
Accepted: Oct 14, 2023
Published print: Dec 27, 2023
Published online: Mar 14, 2024
Identifiers:
Web of science: WOS:001184111000001
Scopus: 2-s2.0-85187910747
Elibrary: 65123264
OpenAlex: W4392796445
Citing: Пока нет цитирований
Altmetrics: