Reference : A discrete competitive facility location model with variable attractiveness
Scientific journals : Article
Business & economic sciences : Production, distribution & supply chain management
http://hdl.handle.net/2268/138700
A discrete competitive facility location model with variable attractiveness
English
Kucukaydin, Hande [Université de Liège - ULiège > HEC-Ecole de gestion : UER > UER Opérations : Supply Chain Management >]
Aras, Necati [> >]
Altinel, I. Kuban [> >]
2011
Journal of the Operational Research Society
Stockton Press
62
9
1726-1741
Yes (verified by ORBi)
International
0160-5682
Houndmills
United Kingdom
[en] competitive facility location ; variable facility attractiveness ; mixed-integer nonlinear pogramming ; Lagrangean heuristic ; branch-and-bound
[en] We consider the discrete version of the competitive facility location problem in which new facilities have to be located by a new market entrant firm to compete against already existing facilities that may belong to one or more competitors. The demand is assumed to be aggregated at certain points in the plane and the new facilities can be located at predetermined candidate sites. We employ Huff’s gravity-based rule in modelling the behaviour of the customers where the probability that customers at a demand point patronize a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. The objective of the firm is to determine the locations of the new facilities and their attractiveness levels so as to maximize the profit, which is calculated as the revenue from the customers less the fixed cost of opening the facilities and variable cost of setting their attractiveness levels. We formulate a mixed-integer nonlinear programming model for this problem and propose three methods for its solution: a Lagrangean heuristic, a branch-and-bound method with Lagrangean relaxation, and another branch-and-bound method with nonlinear programming relaxation. Computational results obtained on a set of randomly generated instances show that the last method outperforms the others in terms of accuracy and efficiency and can provide an optimal solution in a reasonable amount of time.
http://hdl.handle.net/2268/138700
10.1057/jors.2010.136

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