Advertisement

Optimizing Spatial Accessibility of Company Branches Network with Constraints

  • Oleg ZaikinEmail author
  • Ivan Derevitskii
  • Klavdiya Bochenina
  • Janusz Holyst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

The ability of customer data collection in enterprise corporate information systems leads to the emergence of customer-centric algorithms and approaches. In this study, we consider the problem of choosing a candidate branch for closing based on the overall expected level of dissatisfaction of company customers with the location of remaining branches. To measure the availability of branches for individuals, we extract points of interests from the traces of visits using the clustering algorithm to find centers of interests. The following questions were further considered: (i) to which extent does spatial accessibility influence the choice of company branches by the customers? (ii) which algorithm provides better trade-off between accuracy and computational complexity? These questions were studied in application to a bank branches network. In particular, data and domain restrictions from our bank-partner (one of the largest regional banks in Russia) were used. The results show that: (i) spatial accessibility significantly influences customers’ choice (65%–75% of customers choose one of the top 5 branches by accessibility after closing a branch), (ii) the proposed greedy algorithm provides on optimal solution in almost all of cases, (iii) output of the greedy algorithm may be further improved with a local search algorithm, (iv) instance of a problem with several dozens of branches and up to million customers may be solved with near-optimal quality in dozens of seconds.

Keywords

Branch network optimization Location Spatial accessibility Banking Black-box optimization 

Notes

Acknowledgments

This research is financially supported by the Russian Science Foundation, Agreement 17-71-30029 with co-financing of Bank Saint Petersburg.

References

  1. 1.
    Abualigah, L., Hanandeh, E.: Applying genetic algorithms to information retrieval using vector space model. Int. J. Comput. Sci. Eng. Appl. 5, 19–28 (2015).  https://doi.org/10.5121/ijcsea.2015.5102CrossRefGoogle Scholar
  2. 2.
    Allahi, S., Mobin, M., Vafadarnikjoo, A., Salmon, C.: An integrated AHP-GIS-MCLP method to locate bank branches. In: Proceedings of Industrial and Systems Engineering Research Conference, July 2015Google Scholar
  3. 3.
    Basar, A., Kabak, O., Topcu, Y.I.: A tabu search algorithm for multi-period bank branch location problem: a case study in a Turkish bank. Scientia Iranica (2018).  https://doi.org/10.24200/sci.2018.20493CrossRefGoogle Scholar
  4. 4.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD 1996, pp. 226–231. AAAI Press (1996)Google Scholar
  5. 5.
    Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986).  https://doi.org/10.1016/0305-0548(86)90048-1MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Miliotis, P., Dimopoulou, M., Giannikos, I.: A hierarchical location model for locating bank branches in a competitive environment. Int. Trans. Oper. Res. 9, 549–565 (2002).  https://doi.org/10.1111/1475-3995.00373CrossRefzbMATHGoogle Scholar
  7. 7.
    Monteiro, M.S.R., Fontes, D.B.M.M.: Locating and sizing bank-branches by opening, closing or maintaining facilities. In: Haasis, H.D., Kopfer, H., Schönberger, J. (eds.) Operations Research Proceedings 2005, pp. 303–308. Springer, Heidelberg (2006).  https://doi.org/10.1007/3-540-32539-5_48CrossRefGoogle Scholar
  8. 8.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Englewood cliffs (2009)zbMATHGoogle Scholar
  9. 9.
    Schneider, S., Seifert, F., Sunyaev, A.: Market potential analysis and branch network planning: application in a german retail bank. In: 2014 47th Hawaii International Conference on System Sciences, pp. 1122–1131, January 2014.  https://doi.org/10.1109/HICSS.2014.145
  10. 10.
    Thiel, D., Hovelaque, V., Pham, D.N.: A multi-agent model for optimizing supermarkets location in emerging countries. In: 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 395–399 (2012).  https://doi.org/10.1109/CINTI.2012.6496798
  11. 11.
    Wang, Q., Batta, R., Bhadury, J., Rump, C.M.: Budget constrained location problem with opening and closing of facilities. Comput. Oper. Res. 30(13), 2047–2069 (2003).  https://doi.org/10.1016/S0305-0548(02)00123-5MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Wu, S.S., Kuang, H., Lo, S.M.: Modeling shopping center location choice: Shopper preference-based competitive location model. J. Urban Plan. Dev. 145, March 2019.  https://doi.org/10.1061/(ASCE)UP.1943-5444.0000482CrossRefGoogle Scholar
  13. 13.
    Xia, L., Xie, M., Yin, W., Dong, J., Shao, J.: Markov decision processes formulation for stochastic and dynamic bank branches location problems. In: Proceedings of 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, IEEE/SOLI 2008, vol. 1, pp. 419–424, October 2008.  https://doi.org/10.1109/SOLI.2008.4686432
  14. 14.
    Xie, W., Ouyang, Y., Somani, K.: Optimizing location and capacity for multiple types of locomotive maintenance shops. Comp.-Aided Civ. Infrastruct. Eng. 31(3), 163–175 (2016).  https://doi.org/10.1111/mice.12114CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ITMO UniversitySaint PetersburgRussia
  2. 2.Faculty of PhysicsWarsaw University of TechnologyWarsawPoland

Personalised recommendations