A Hierarchical Location Model for Locating Bank Branches in a Competitive Environment

Miliotis, P.; Dimopoulou, Maria; Giannikos, Ioannis

doi:10.1111/1475-3995.00373

Cited by 43 publications

(23 citation statements)

References 15 publications

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“…They used the weighted method to develop an integer linear programming model to obtain Pareto optimal locations associated with the inner competition between the owners of the old facilities and the owners of the new facilities. Miliotis et al (2002) explained how demand-covering models could be combined with geographical information systems (GIS) to determine the optimal location of bank branches, taking into account the different factors that characterize local conditions within the demand area.…”

Section: Models With Inelastic Demandmentioning

confidence: 99%

Competitive location: a state-of-art review

Ashtiani¹

2016

10.5267/j.ijiec

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This paper provides a review on recent works in the field of competitive facility location models based on the following seven components: 1) Variables, 2) Competition type, 3) Solution space, 4) Customer behavior, 5) Demand type, 6) Number of new facilities and 7) Relocation and redesign possibility. First, the components are introduced and then based on these components; different studies are compared with each other via a proposed taxonomy and finally a review on work of each paper is provided.

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Section: Models With Inelastic Demandmentioning

confidence: 99%

Competitive location: a state-of-art review

Ashtiani¹

2016

10.5267/j.ijiec

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“…In the past several decades, extensive work has been carried out on facility related business analysis and decision making research, covering varying topics as performance evaluation (Bala and Cook, 2003;Chen, 2002;Yin, Chen and Dong, 2006), customer segmentation (Marcus, 1998), demand forecasting (Chang, 1995, site location (Jaramillo, Bhadury and Batta, 2002;Miliotis, Dimopoulou and Giannikos, 2002;ReVelle and Eiselt, 2005), trade area analysis (Hansen and Weinberg, 1979;Dong, Yin, and Chen, 2006;Kosmidou, Pasiouras, and et al, 2006), etc. GIS products, such as ArcGIS (ESRI) and MapInfo (MapInfo) have been used in facility network transformation analysis.…”

Section: Facility Related Business Analysis and Decision Making Researchmentioning

confidence: 99%

iFAO: Facility Network Transformation Services for Specific Customer Oriented Service Industries

Dong¹,

Bai²,

Yin³

et al. 2009

Service Science

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acility Network Transformation (FNT) is a strategic approach to assess and optimize industrial facility networks for new site selection, demand forecasting, and performance evaluation in customer oriented service industries such as banking and retail. In practice, FNT requirements are often diverse, dynamic and industry specific. It is often difficult to implement a generic FNT service fully integrated with legacy systems. The heterogeneity of spatial information further calls for a loosely coupled architecture. An innovative spatial decision support system, iFAO (Intelligent Facility Network Analytics and Optimization), for FNT problems using Service Oriented Architecture principles was developed. In this paper, key FNT service patterns are identified and modeled to provide an industry independent solution, and an SOA-based framework for iFAO is proposed. An underlying data model design is elaborated which acts as a common language for experts from different domains to communicate and help speed up the solution development. A retail case is presented to illustrate how SOA based iFAO services are integrated to address real industrial problems, especially for quick decisions on business strategy in the competitive and everchanging marketplace.

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“…A comprehensive literature review was conducted for determining an initial list involving both qualitative and quantitative criteria [1], [3], [4], [5], [14], [27], [28], [29], [31], [32], [34], [36]. Then, the initial list was discussed with the bank's managers on the face to face meetings.…”

Section: A Building the Hierarchical Modelmentioning

confidence: 99%

A decision support model for location selection: Bank branch case

Çebi

Zeren

2008

PICMET '08 - 2008 Portland International Conference on Management of Engineering &Amp; Technology

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Branch location selection is one of the most important and strategic decision making processes requiring to asses carefully several criteria based on the mission and strategy. The study aims to provide a decision support model in order to help bank managers evaluate and select the most appropriate location for a bank branch on the basis of a case study in a banking institution in Turkey. The objective of the bank is to select the most appropriate city for opening a branch among the six alternatives in the Eastern part of Turkey. Because of the multi-criteria nature of the problem and the fuzziness in the comparisons of the criteria and the alternatives, Fuzzy Analytic Hierarchy Process was used to address the problem of location selection in the study. The model was consisted of five main criteria -"demographic", "socio-economic", "banking indicators", " sectoral distribution of employment ", and "regional trade potential"-and seventeen sub-criteria representing utmost attention of management based on the bank's vision, mission, and strategy.

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A Hierarchical Location Model for Locating Bank Branches in a Competitive Environment

Cited by 43 publications

References 15 publications

Competitive location: a state-of-art review

Competitive location: a state-of-art review

iFAO: Facility Network Transformation Services for Specific Customer Oriented Service Industries

A decision support model for location selection: Bank branch case

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