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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbasi GY (2003) A decision support system for bank location selection. Int J Comput Appl Technol 16:202–210
Ahsan MK, Bartlema J (2004) Monitoring healthcare performance by analytic hierarchy process: a developing-country perspective. Int Trans Oper Res 11:465–478
Alexandris G, Giannikos I (2010) A new model for maximal coverage exploiting GIS capabilities. Eur J Oper Res 202:328–338
Arabani AB, Farahani RZ (2012) Facility location dynamics: an overview of classifications and applications. Comput Ind Eng 62:408–420
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
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
Badri MA (2001) A combined AHP—GP model for quality control systems. Int J Product Econ 72:27–40
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
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
Başar A, Çatay B, Ünlüyurt T (2012) A taxonomy for emergency service station location problem. Optim Lett 6(6):1147–1160
Berman O, Krass D (2002) The generalized maximal covering location problem. Comp Oper Res 29:563–581
Boufounou PV (1995) Evaluating bank branch location and performance: a case study. Eur J Oper Res 87:389–402
Brimberg J, Drezner Z (2013) A new heuristic for solving the p-median problem in the plane. Comp Oper Res 40:427–437
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
Carlsson C, Fuller R (1996) Fuzzy multiple criteria decision making: recent developments. Fuzzy Sets Syst 78:139–153
Chen SJ, Hwang CL (1993) Fuzzy multiple attribute decision-making, methods and applications. Lecture notes in Economics and Mathematical systems 375. Springer, Heidelberg
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
Church R, ReVelle C (1974) The maximal covering location problem. Pap Reg Sci Assoc 32:101–118
Cinar N (2009) A decision support model for bank branch location selection. World Acad Sci Eng Technol 60:126–131
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)
Clawson CJ (1974) Fitting branch locations, performance standards, and marketing strategies to local conditions. J Marketing 38:8–14
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
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
Daskin M (1983) A maximum expected covering location model: formulation, properties and heuristic solution. Transp Sci 17:48–70
Daskin MS, Stern EH (1981) A hierarchical objective set covering model for emergency medical service vehicle deployment. Transp Sci 15:137–152
Doyle P, Fenwick I, Savage GP (1981) A model for evaluating branch location and performance. J Bank Res 12:90–95
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
Fernandez I, Ruiz MC (2009) Descriptive model and evaluation system to locate sustainable industrial areas. J Cleaner Prod 17(1):87–100
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
Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8:156–166
Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206
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
Hakimi S (1964) Optimum locations of switching centres and the absolute centres and medians of a graph. Oper Res 12:450–459
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
Huff D (1963) A probabilistic analysis of shopping center trade areas. Land Econ 39:81–90
Hwang H (2002) Design of supply-chain logistics system considering service level. Comput Industrial Eng 43:283–297
Ishizaka A, Lusti M (2004) An expert module to improve the consistency of AHP matrices. Int Trans Oper Res 11:97–105
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
Kaufman G, Mote R (1994) A review from the Federal Reserve Bank of Chicago. Federal Reserve Bank of Chicago, Chicago
Kuehn A, Hamburger M (1960) A heuristic program for locating warehouses. Manage Sci 9:643–666
Malczewski J (1999) GIS and multicriteria decision making. Wiley, New York
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
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
Manne A (1964) Plant location under economies of scale. decentralization and computation. Manage Sci 11:213–235
Marianov V, ReVelle CS (1995) Facility location. Springer, Berlin
Meidan A (1983) Distribution of bank services and branch location. Int J Phys Distrib Managerial Manage 13(3):5–18
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
Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97
Min H (1989) A model based decision support system for locating banks. Inform Manag 17:207–215
Min H, Melachrinoudis E (2001) The three-hierarchical location-allocation of banking facilities with risk and uncertainty. Int Trans Oper Res 8:381–401
Morrison PS, O’Brien R (2001) Bank branch closures in New Zealand: the application of a spacial interaction model. Appl Geogr 21:301–330
Murray AT, Tong D, Kim K (2010) Enhancing classic coverage location models. Int Reg Sci Rev 33(2):115–133
Olsen LM, Lord JD (1979) Market area characteristics and branch bank performance. J Bank Res Summer 10:102–110
Osman IH, Kelly JP (1996) Meta-heuristics: theory and applications. Kluwer Academic Publishers, Boston
Pardalos PM, Resende MGC (2002) Handbook of applied optimization. Oxford University Press, New York
Pastor JT (1994) Bicriterion programs and managerial location decisions: application to the banking sector. J Oper Res Soc 45(12):1351–1362
Portela MCAS, Thanassoulis E (2007) Comparative efficiency analysis of Portuguese bank branches. Eur J Oper Res 177:1275–1288
Rahgan SH, Mirzazadeh A (2012) A new method in the location problem using fuzzy evidential reasoning. Eng Techno 4(22):4636–4645
Ravallion M, Wodon Q (2000) Banking on the poor? Branch location and nonfarm rural development in Bangladesh. Rev Devel Econ 4:121–139
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
Ribeiro CC, Hansen P (2002) Essays and surveys in metaheuristics. Kluwer Academic Publishers, Norwell
Saaty TL (1980) The analytic hierarchy process. McGraw-Hill Inc, New York
Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26
Sato Y (2004) Comparison between multiple-choice and analytic hierarchy process: measuring human perception. Int Trans in Ope Res 11:77–86
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
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
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
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
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
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
Youssef H, Sait SM, Adiche H (2001) Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Eng Appl Artif Intell 14:167–181
Zhang L, Rushton G (2008) Optimizing the size and locations of facilities in competitive multi-site service systems. Comput Oper Res 35:327–338
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-3-319-20282-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20281-5
Online ISBN: 978-3-319-20282-2
eBook Packages: Business and EconomicsBusiness and Management (R0)