Modeling international facility location under uncertainty: A review, analysis, and insights

Mouna Kchaou Boujelben and Youssef Boulaksil

IISE Transactions, 2018, vol. 50, issue 6, 535-551

Abstract: In this article, we focus on international facility location models. First, we conduct an extensive literature review on the subject and we propose a classification of the surveyed papers. The classification includes the modeling approach used, international factors, as well as dynamic and stochastic aspects of the approach. Based on the literature review, we find that international facility location problems received little attention. In particular, dynamic facility location models under uncertainty have been hardly studied. Therefore, we develop a stochastic dynamic international facility location model, using a Mixed-Integer Linear Programming (MILP) formulation. Through a case study, we show that international factors, as well as the dynamic and stochastic components of the problem, might influence strategic location decisions. We also quantify the added value of using a stochastic model instead of a deterministic counterpart and we derive insights regarding policies that governments can use to attract investments.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2017.1408165 (text/html)
Access to full text is restricted to subscribers.

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:taf:uiiexx:v:50:y:2018:i:6:p:535-551

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2017.1408165

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().