Two-Swarm PSO for Competitive Location Problems

Author(s):  
Clara M. Campos Rodríguez ◽  
José A. Moreno Pérez ◽  
Hartmut Noltemeier ◽  
Dolores R. Santos Peñate
2006 ◽  
Vol 05 (03) ◽  
pp. 531-543 ◽  
Author(s):  
FENGMEI YANG ◽  
GUOWEI HUA ◽  
HIROSHI INOUE ◽  
JIANMING SHI

This paper deals with two bi-objective models arising from competitive location problems. The first model simultaneously intends to maximize market share and to minimize cost. The second one aims to maximize both profit and the profit margin. We study some of the related properties of the models, examine relations between the models and a single objective parametric integer programming problem, and then show how both bi-objective location problems can be solved through the use of a single objective parametric integer program. Based on this, we propose two methods of obtaining a set of efficient solutions to the problems of fundamental approach. Finally, a numerical example is presented to illustrate the solution techniques.


2007 ◽  
Vol 167 (1) ◽  
pp. 87-105 ◽  
Author(s):  
J. L. Redondo ◽  
J. Fernández ◽  
I. García ◽  
P. M. Ortigosa

2017 ◽  
Vol 79 ◽  
pp. 12-18 ◽  
Author(s):  
Pascual Fernández ◽  
Blas Pelegrín ◽  
Algirdas Lančinskas ◽  
Julius Žilinskas

2019 ◽  
Vol 8 (5) ◽  
pp. 202 ◽  
Author(s):  
Wei Jiang ◽  
Yandong Wang ◽  
Mingxuan Dou ◽  
Senbao Liu ◽  
Shiwei Shao ◽  
...  

Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity evaluations. Most studies based on location data assumed the customers’ sensitivities to be global and constant over space. In this paper, we proposed a new method of using social media data to solve competitive location problems based on the evaluation of customers’ local sensitivities. Regular units were first designed to spatially aggregate social media data to extract samples with uniform spatial distribution. Then, geographically weighted regression (GWR) and the Huff model were combined to evaluate local sensitivities. By applying the evaluation results, the captures for different feasible locations were calculated, and the optimal location for a new retail facility could be determined. In our study, the five largest retail agglomerations in Beijing were taken as test cases, and a possible new retail agglomeration was located. The results of our study can help people have a better understanding of the spatial variation of customers’ local sensitivities. In addition, our results indicate that our method can solve competitive location problems in a cost-effective way.


2013 ◽  
Vol 30 (02) ◽  
pp. 1250050 ◽  
Author(s):  
KE FU ◽  
ZHAOWEI MIAO ◽  
JIAYAN XU

A medianoid problem is a competitive location problem that determines the locations of a number of new service facilities that are competing with existing facilities for service to customers. This paper studies the medianoid problem on the plane with Manhattan distance. For the medianoid problem with binary customer preferences, i.e., a case where customers choose the closest facility to satisfy their entire demand, we show that the general problem is NP-hard and present solution methods to solve various special cases in polynomial time. We also show that the problem with partially binary customer preferences can be solved with a similar approach we develop for the model with binary customer preferences.


2018 ◽  
Vol 265 (3) ◽  
pp. 872-881 ◽  
Author(s):  
José Gentile ◽  
Artur Alves Pessoa ◽  
Michael Poss ◽  
Marcos Costa Roboredo

2012 ◽  
Vol 8 (2) ◽  
pp. 555-567 ◽  
Author(s):  
A. G. Arrondo ◽  
J. Fernández ◽  
J. L. Redondo ◽  
P. M. Ortigosa

2008 ◽  
Vol 23 (5) ◽  
pp. 779-791 ◽  
Author(s):  
Juana L. Redondo ◽  
José Fernández ◽  
Inmaculada García ◽  
Pilar M. Ortigosa

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