The Computational Techniques for Optimal Store Placement: A Review

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10961)


In today’s world which is subject to an increasing number of stores and level of rivalry on a daily basis, decisions concerning a store’s location are considered highly important. Over the years, researchers and marketers have used a variety of different approaches for solving the optimal store location problem. Like many other research areas, earlier methods for site selection involved the use of statistical data whereas recent methods rely on the rich content which can be extracted from big data via modern data analysis techniques. In this paper, we begin with assessing the historical precedent of the most accepted and applied traditional computational methods for determining a desirable place for a store. We proceed by discussing some of the technological advancements that has led to the advent of more cutting-edge data-driven methods. Finally, we extend a review of some of the most recent, location based social network data-based approaches, to solving the store site selection problem.


Computational movement analysis Location based social network data Geo-marketing Retail store location Site selection 


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Management, Faculty of Social Science and EconomicsAlzahra UniversityTehranIran
  2. 2.Department of Surveying and Geomatic Engineering, Faculty of EngineeringUniversity of TehranTehranIran

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