Competitive location: a state-of-art review

Ashtiani, Milad Gorji

doi:10.5267/j.ijiec.2015.8.002

Cited by 21 publications

(14 citation statements)

References 66 publications

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“…They rather assume that facility attractiveness levels are randomly distributed in the sense that customers have varying perception for the design of facilities and develop two solution procedures for locating p number of facilities. A detailed overview of CFL models can also be found in [24]. This study categorizes the CFL problems using seven components, namely decision variables, competition type, solution space, customer behavior, demand type, number of new facilities to locate, and the possibility for relocation and redesigning of existing facilities.…”

Section: The Book By Farahani and Hekmatfarmentioning

confidence: 99%

Determining and evaluating new store locations using remote sensing and machine learning

Höke¹,

Turgay²,

Ünsalan³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation which is highly dependent on population of the corresponding region and hence volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semi-global matching and mask R-CNN algorithms for this purpose. Using the existing store data, we construct models for estimating the revenue based on surrounding building volumes. In order to choose the right location, the suitable utility model which calculates store revenues should be rigorously determined. Moreover, model parameters should be assessed as correctly as possible. Instead of using randomly generated parameters, we employ remote sensing, computer vision, and machine learning techniques which provide a novel way for evaluating new store locations.

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Section: The Book By Farahani and Hekmatfarmentioning

confidence: 99%

Determining and evaluating new store locations using remote sensing and machine learning

Höke¹,

Turgay²,

Ünsalan³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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“…ere are various competitive facility location models depending on the components to be considered. ese components usually include competitive type, location space, and customer behavior (see survey paper [4]).…”

Section: Introductionmentioning

confidence: 99%

Robust Model of Discrete Competitive Facility Location Problem with Partially Proportional Rule

2020

Mathematical Problems in Engineering

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When consumers faced with the choice of competitive chain facilities that offer exclusive services, current rules cannot describe these customers’ behaviors very well. So we propose a partially proportional rule to represent this kind of customer behavior. In addition, the exact demands of customers in many real-world environments are often difficult to determine. This is contradicting to the assumption in most studies of the competitive facility location problem. For the competitive facility location problem with the partially proportional rule, we establish a robust optimization model to handle the uncertainty of customers’ demands. We propose two methods to solve the robust model by studying the properties of the counterpart problem. The first method MIP is presented by solving a mixed-integer optimization model of the counterpart problem directly. The second method SAS is given by embedding a sorting subalgorithm into the simulated annealing framework, in which the sorting subalgorithm can easily solve the subproblem. The effects of the budget and the robust control parameter to the location scheme are analyzed in a quasi-real example. The result shows that changes in the robust control parameter can affect the customer demands that were captured by the new entrants, thereby changing the optimal solution for facility location. In addition, there is a threshold of the robust control parameter for any given budget. Only when the robust control parameter is larger than this threshold, the market share captured by the new entering firm increases with the increases of this parameter. Finally, numerical experiments show the superiority of the algorithm SAS in large-scare competitive facility location problems.

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“…Ashtiani [29] indicated that some new features (such as relocation, design etc.) are included in the recent progress of competitive facility location problem.…”

Section: Introductionmentioning

confidence: 99%

A leader-follower model for discrete competitive facility location problem under the partially proportional rule with a threshold

2019

PLoS ONE

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When consumers are faced with the choice of competitive chain facilities that offer exclusive services, current rules do not properly describe the behavior pattern of these consumers. To eliminate the gap between the current rules and this kind of customers behavior pattern, the partially proportional rule with a threshold is proposed in this paper. A leader-follower model for discrete competitive facility location problem is established under the partially proportional rule with a threshold. Combining with the greedy strategy and the 2-opt strategy, a heuristical algorithm (GFA) is designed to solve the follower’s problem. By embedding the algorithm (GFA), an improved ranking-based algorithm (IRGA) is proposed to solve the leader-follower model. Numerical tests show that the algorithm proposed in this paper can solve the leader-follower model for discrete competitive facility location problem effectively. The effects of different parameters on the market share captured by the leader firm and the follower firm are analyzed in detail using a quasi-real example. An interesting finding is that in some cases the leader firm does not have a first-mover advantage.

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Competitive location: a state-of-art review

Cited by 21 publications

References 66 publications

Determining and evaluating new store locations using remote sensing and machine learning

Determining and evaluating new store locations using remote sensing and machine learning

Robust Model of Discrete Competitive Facility Location Problem with Partially Proportional Rule

A leader-follower model for discrete competitive facility location problem under the partially proportional rule with a threshold

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