Sparse facility location and network design problems

Gao-Xi Li, Yi Ren and Peiru Yi

Omega, 2025, vol. 136, issue C

Abstract: To further minimize the number of facilities even if it entails additional costs, policymakers often have this preference during facility location decisions. To cater to this preference, we introduce a sparsity-inducing term in this paper. This term generates sparse solutions for both the facility location model and the facility network design model, leading to the proposal of a sparse facility location model and a sparse facility network design model. These two sparse models are formulated as nonlinear mixed-integer programs, featuring objective functions that are non-Lipschitz continuous concerning continuous variables, making them highly challenging to solve. Consequently, we propose a continuous relaxation approach that converts these sparse discrete models into continuous nonlinear programs. We validate the efficacy of both the sparse discrete models and the relaxation method through two classic case studies.

Keywords: Sparse facility location; Nonlinear programs; Non-Lipschitz continuous; Mixed integer programs (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0305048325000453
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:136:y:2025:i:c:s0305048325000453

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.omega.2025.103319

Access Statistics for this article

Omega is currently edited by B. Lev

More articles in Omega from Elsevier
Bibliographic data for series maintained by Catherine Liu ().