Skip to main content

Location Analysis in Banking: A New Methodology and Application For a Turkish Bank

  • Chapter
  • First Online:
Applications of Location Analysis

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 232))

Abstract

In recent years, technology has improved and distribution channels such as credit cards, telephone-internet banking, Automated Teller Machines (ATMs) etc. have become alternative opportunities for reaching services of banks. However, banks generally gain new customers and develop customers’ loyalty at their branches. Since branches are the indispensable contact points between the banks and their customers, no bank can easily avoid opening new branches or reorganizing the locations of current ones. According to the current statistics of The Banks Association of Turkey, the number of total bank branches has increased by 5.35 % from 10,450 to 11,009 in the last year. According to the statistics of Retail Banker International, JPMorgan & Chase opened 89 new branches in June 2013, which increased number of its branches from 5,608 to 5,697. In the same period, BB&T increased the number of its branches from 1,775 to 1,851 (Retail Banker International 2013). This shows that, due to the effects of increase in total population, population per bank branch and individual earnings, banks try to increase the number of their branches by locating them in the right places. Therefore, the branch location problem is a fundamental topic for banks in reaching their strategic goals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abbasi GY (2003) A decision support system for bank location selection. Int J Comput Appl Technol 16:202–210

    Article  Google Scholar 

  • Ahsan MK, Bartlema J (2004) Monitoring healthcare performance by analytic hierarchy process: a developing-country perspective. Int Trans Oper Res 11:465–478

    Article  Google Scholar 

  • Alexandris G, Giannikos I (2010) A new model for maximal coverage exploiting GIS capabilities. Eur J Oper Res 202:328–338

    Article  Google Scholar 

  • Arabani AB, Farahani RZ (2012) Facility location dynamics: an overview of classifications and applications. Comput Ind Eng 62:408–420

    Article  Google Scholar 

  • Aras H, Erdogmuş S, Koç E (2004) Multi-criteria selection for a wind observation station location using analytic hierarchy process. Renewable Energy 29(8):1383–1392

    Article  Google Scholar 

  • Arostegui MA, Kadipasaoglu SN, Khumawala BM (2006) An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems. Int J Product Econ 103(2):742–754

    Article  Google Scholar 

  • Badri MA (2001) A combined AHP—GP model for quality control systems. Int J Product Econ 72:27–40

    Article  Google Scholar 

  • Banking and sector information (2014) http://www.tbb.org.tr/tr/banka-ve-sektor-bilgileri/banka-bilgileri/subeler/65. Accessed 23 June 2014

  • Baron O, Berman O, Kim S, Krass D (2009) Ensuring feasibility in location problems with stochastic demands and congestion. IIE Trans 41:467–481

    Article  Google Scholar 

  • Başar A, Çatay B, Ünlüyurt T (2011) A multi-period double coverage approach for locating the emergency medical service stations in Istanbul. J Oper Res Soc 62(4):627–637

    Article  Google Scholar 

  • Başar A, Çatay B, Ünlüyurt T (2012) A taxonomy for emergency service station location problem. Optim Lett 6(6):1147–1160

    Article  Google Scholar 

  • Berman O, Krass D (2002) The generalized maximal covering location problem. Comp Oper Res 29:563–581

    Article  Google Scholar 

  • Boufounou PV (1995) Evaluating bank branch location and performance: a case study. Eur J Oper Res 87:389–402

    Article  Google Scholar 

  • Brimberg J, Drezner Z (2013) A new heuristic for solving the p-median problem in the plane. Comp Oper Res 40:427–437

    Article  Google Scholar 

  • Camanho AS, Dyson RG (2005) Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments. Eur J Oper Res 161:432–446

    Article  Google Scholar 

  • Carlsson C, Fuller R (1996) Fuzzy multiple criteria decision making: recent developments. Fuzzy Sets Syst 78:139–153

    Article  Google Scholar 

  • Chen SJ, Hwang CL (1993) Fuzzy multiple attribute decision-making, methods and applications. Lecture notes in Economics and Mathematical systems 375. Springer, Heidelberg

    Google Scholar 

  • Chen H, Barna BA, Rogers TN, Shonnard DR (2001) A screening methodology for improved solvent selection using economic and environmental assessments. Clean Prod Processes 3:290–302

    Article  Google Scholar 

  • Church R, ReVelle C (1974) The maximal covering location problem. Pap Reg Sci Assoc 32:101–118

    Article  Google Scholar 

  • Cinar N (2009) A decision support model for bank branch location selection. World Acad Sci Eng Technol 60:126–131

    Google Scholar 

  • Cinar N, Ahiska SS (2010) A decision support model for bank branch location selection. In YAEM 2010. Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management, Sabancı University, Istanbul, June 30-July 2, (CD-ROM)

    Google Scholar 

  • Clawson CJ (1974) Fitting branch locations, performance standards, and marketing strategies to local conditions. J Marketing 38:8–14

    Article  Google Scholar 

  • Cook WD, Seiford LM, Zhu J (2004) Models for performance benchmarking: measuring the effect of e-business activities on banking performance. Omega 32:313–322

    Article  Google Scholar 

  • Curtin KM, Hayslett-McCall K, Qiu F (2010) Determining optimal police patrol areas with maximal covering and backup covering location models. Netw Spat Econ 10(1):125–145

    Article  Google Scholar 

  • Daskin M (1983) A maximum expected covering location model: formulation, properties and heuristic solution. Transp Sci 17:48–70

    Article  Google Scholar 

  • Daskin MS, Stern EH (1981) A hierarchical objective set covering model for emergency medical service vehicle deployment. Transp Sci 15:137–152

    Article  Google Scholar 

  • Doyle P, Fenwick I, Savage GP (1981) A model for evaluating branch location and performance. J Bank Res 12:90–95

    Google Scholar 

  • Erdemir ET, Batta R, Spielman S, Rogerson PA, Blatt A, Flanigan M (2010) Joint ground and air emergency medical services coverage models: a greedy heuristic solution approach. Eur J Oper Res 207:736–749

    Article  Google Scholar 

  • Fernandez I, Ruiz MC (2009) Descriptive model and evaluation system to locate sustainable industrial areas. J Cleaner Prod 17(1):87–100

    Article  Google Scholar 

  • Gendreau M, Laporte G, Semet F (2000) A dynamic model and parallel tabu search heuristic for real time ambulance relocation. Parallel Comput 27:1641–1653

    Article  Google Scholar 

  • Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8:156–166

    Article  Google Scholar 

  • Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206

    Article  Google Scholar 

  • Grabowski J, Wodecki M (2004) A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion. Comput Oper Res 31:1891–1909

    Article  Google Scholar 

  • Hakimi S (1964) Optimum locations of switching centres and the absolute centres and medians of a graph. Oper Res 12:450–459

    Article  Google Scholar 

  • Hansen P, Brimberg J, Urosevic D, Mladenovic N (2009) Solving large p-median clustering problems by primal-dual variable neighborhood search. Data Min Knowl Disc 19:351–375

    Article  Google Scholar 

  • Huff D (1963) A probabilistic analysis of shopping center trade areas. Land Econ 39:81–90

    Article  Google Scholar 

  • Hwang H (2002) Design of supply-chain logistics system considering service level. Comput Industrial Eng 43:283–297

    Article  Google Scholar 

  • Ishizaka A, Lusti M (2004) An expert module to improve the consistency of AHP matrices. Int Trans Oper Res 11:97–105

    Article  Google Scholar 

  • Jablonsky J, Fiala P, Smirlis Y, Despotis DK (2004) DEA with interval data: an illustration using the evaluation of branches of a Czech bank. Central Eur J Oper Res 12:323–337

    Google Scholar 

  • Kaufman G, Mote R (1994) A review from the Federal Reserve Bank of Chicago. Federal Reserve Bank of Chicago, Chicago

    Google Scholar 

  • Kuehn A, Hamburger M (1960) A heuristic program for locating warehouses. Manage Sci 9:643–666

    Article  Google Scholar 

  • Malczewski J (1999) GIS and multicriteria decision making. Wiley, New York

    Google Scholar 

  • Malek M, Guruswamy M, Pandya M, Owens H (1989) Serial and parallel simulated annealing and tabu search algorithms for the traveling salesman problem. Ann Oper Res 21:59–84

    Article  Google Scholar 

  • Manandhar R, Tang JCS (2002) The evaluation of bank branch performance using data envelopment analysis: a framework. J High Technol Manage Res 13:1–17

    Article  Google Scholar 

  • Manne A (1964) Plant location under economies of scale. decentralization and computation. Manage Sci 11:213–235

    Article  Google Scholar 

  • Marianov V, ReVelle CS (1995) Facility location. Springer, Berlin

    Google Scholar 

  • Meidan A (1983) Distribution of bank services and branch location. Int J Phys Distrib Managerial Manage 13(3):5–18

    Article  Google Scholar 

  • Miliotis P, Dimopoulou M, Giannikos I (2002) A hierarchial location model for locating bank branches in a competitive environment. Int Trans Oper Res 9:549–565

    Article  Google Scholar 

  • Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97

    Article  Google Scholar 

  • Min H (1989) A model based decision support system for locating banks. Inform Manag 17:207–215

    Article  Google Scholar 

  • Min H, Melachrinoudis E (2001) The three-hierarchical location-allocation of banking facilities with risk and uncertainty. Int Trans Oper Res 8:381–401

    Article  Google Scholar 

  • Morrison PS, O’Brien R (2001) Bank branch closures in New Zealand: the application of a spacial interaction model. Appl Geogr 21:301–330

    Article  Google Scholar 

  • Murray AT, Tong D, Kim K (2010) Enhancing classic coverage location models. Int Reg Sci Rev 33(2):115–133

    Article  Google Scholar 

  • Olsen LM, Lord JD (1979) Market area characteristics and branch bank performance. J Bank Res Summer 10:102–110

    Google Scholar 

  • Osman IH, Kelly JP (1996) Meta-heuristics: theory and applications. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  • Pardalos PM, Resende MGC (2002) Handbook of applied optimization. Oxford University Press, New York

    Book  Google Scholar 

  • Pastor JT (1994) Bicriterion programs and managerial location decisions: application to the banking sector. J Oper Res Soc 45(12):1351–1362

    Google Scholar 

  • Portela MCAS, Thanassoulis E (2007) Comparative efficiency analysis of Portuguese bank branches. Eur J Oper Res 177:1275–1288

    Article  Google Scholar 

  • Rahgan SH, Mirzazadeh A (2012) A new method in the location problem using fuzzy evidential reasoning. Eng Techno 4(22):4636–4645

    Google Scholar 

  • Ravallion M, Wodon Q (2000) Banking on the poor? Branch location and nonfarm rural development in Bangladesh. Rev Devel Econ 4:121–139

    Article  Google Scholar 

  • Retail Banker International (2013) US Branch numbers fall for fourth year running (2014). https://dscqm8cqg6d5o.cloudfront.net/uploads/articles/pdfs/mnetisgnefhmblsrdclkablzye_rbioct13issue694usbranches.pdf. Accessed 23 June 2014

  • ReVelle CS, Swain R (1970) Central facilities location. Geogra Anal 2:30–42

    Article  Google Scholar 

  • Ribeiro CC, Hansen P (2002) Essays and surveys in metaheuristics. Kluwer Academic Publishers, Norwell

    Book  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process. McGraw-Hill Inc, New York

    Google Scholar 

  • Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26

    Article  Google Scholar 

  • Sato Y (2004) Comparison between multiple-choice and analytic hierarchy process: measuring human perception. Int Trans in Ope Res 11:77–86

    Article  Google Scholar 

  • The Banks Association of Turkey (2014) Available at http://www.tbb.org.tr/tr/banka-ve-sektor-bilgileri/banka-bilgileri/subeler/65. Accessed 12 Apr 2015

  • Toregas CR, Swain R, ReVelle CS, Bergman L (1971) The location of emergency service facilities. Oper Res 19:1363–1373

    Article  Google Scholar 

  • Tzeng GH, Teng MH, Chen JJ, Opricovic S (2002) Multi criteria selection for a restaurant location in Taipei. Int J Hosp Manage 21(2):171–187

    Article  Google Scholar 

  • Wang Q, Batta R, Rump CM (2002) Algorithms for a facility location problem with stochastic customer demand and immobile servers. Ann Oper Res 111:17–34

    Article  Google Scholar 

  • Wang Q, Batta R, Bhadury J, Rump CM (2003) Budget constrained location problem with opening and closing of facilities. Comput Oper Res 30:2047–2069

    Article  Google Scholar 

  • Wu CR, Lin CT, Chen HC (2007) Optimal selection of location for Taiwanese hospitals to ensure a competitive advantage by using the analytic hierarchy process and sensitivity analysis. Build Environ 42(3):1431–1444

    Article  Google Scholar 

  • Xia L, Yin W, Dong J, Wu T, Xie M, Zhao Y (2010) Hybrid nested partitions algorithm for banking facility location problems. IEEE Transautomation Sci Eng 7(3):654–658

    Article  Google Scholar 

  • Youssef H, Sait SM, Adiche H (2001) Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Eng Appl Artif Intell 14:167–181

    Article  Google Scholar 

  • Zhang L, Rushton G (2008) Optimizing the size and locations of facilities in competitive multi-site service systems. Comput Oper Res 35:327–338

    Article  Google Scholar 

  • Zhang G, Habenicht W, Spieß WEL (2003) Improving the structure of deep frozen and chilled food chain with tabu search procedure. J Food Eng 60(1):67–79

    Article  Google Scholar 

  • Zhao L, Garner B, Parolin B (2004) Branch bank closures in Sydney: a geographical perspective and analysis. Proceedings of the 12th International Conference on Geoformatics, Sweden. June 7–9:541–548

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Burçin Bozkaya .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Başar, A., Kabak, Ö., Topçu, Y., Bozkaya, B. (2015). Location Analysis in Banking: A New Methodology and Application For a Turkish Bank. In: Eiselt, H., Marianov, V. (eds) Applications of Location Analysis. International Series in Operations Research & Management Science, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-319-20282-2_2

Download citation

Publish with us

Policies and ethics