Abstract
The balanced partitioning of geographic space into regions is a common problem. This Territory Design Problem (TDP) of assigning smaller areas to larger regions with equal potential is a task mainly done manually. Therefore, the result becomes subjective and provides only a roughly approximated balanced result. This work presents an automated allocation of independent areas to regions using the Genetic Algorithm (GA) , which finds an optimally balanced configuration of regions based on multiple criteria. Thereby, spatial constraints are fully respected as (1) all areas remain contiguous within a region and (2) the automated allocation facilitates a compact region shape. The developed algorithm was tested on a case study in the field of sales territory planning. The target of sales territory planning is the optimal distribution of balanced and fair sales areas based on market potentials. Results of our case study demonstrate the effectiveness of our proposed technique to find an optimal structure of sales territories in a reasonable time. The distance that salesmen need to travel is 16% lower than the existing sales territory configuration. This means that the regions are more balanced and more compact. Due to the independent nature of the GA, this method demonstrates a high flexibility to the optimization problem. It can be easily altered to any objective in territory planning as well as to familiar multi-criteria spatial allocation problems in other disciplines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blais, M., Lapierre, S. D., & Laporte, G. (2003). Solving a home-care districting problem in an urban setting. The Journal of the Operational Research Society, 54, 1141–1147.
Cao, R., Li, G., & Wu, Y. (2007). A self-adaptive evolutionary algorithm for multi-objective optimization. In D.-S. Huang, L. Heutte, & M. Loog (Eds.), Advanced intelligent computing theories and applications with aspects of artificial intelligence (pp. 553–564). Berlin, Heidelberg: Springer.
D’amico, S. J., Wang, S.–J., & Batta, R., et al. (2002). A simulated annealing approach to police district design. Computers & Operations Research, 29, 667–684.
Drexl, A., & Haase, K. (1999). Fast approximation methods for sales force deployment. Management Science, 45, 1307–1323.
Fogel, D. B. (1995). Phenotypes, genotypes, and operators in evolutionary computation. In Proceedings of 1995 IEEE International Conference on Evolutionary Computation (Vol. 1).
Fonseca, C. M., & Fleming, P. J. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3, 1–16.
Gil, P. “What is ‘SaaS’ (Software as a Service)?”. Retrieved June 13, 2016 from http://netforbeginners.about.com/od/s/f/what_is_SaaS_software_as_a_service.htm.
Haase, K., & Müller, S. (2014). Upper and lower bounds for the sales force deployment problem with explicit contiguity constraints. European Journal of Operational Research, 237, 677–689.
Kalcsics, J., Nickel, S., & Schröder, M. (2005). Towards a unified territorial design approach—Applications, algorithms and GIS integration. Reports of the Fraunhofer Institute for Industrial Mathematics, 71, 1–56.
Kazimipour, B., Li, X., & Qin, A. K. (2014). A review of population initialization techniques for evolutionary algorithms. In 2014 IEEE Congress on Evolutionary Computation (CEC) (pp. 2585–2592).
Luque, G., & Alba, E. (2011). Parallel genetic algorithms: Theory and real world applications (Vol. 367). Springer.
Płuciennik, T., & Płuciennik-Psota, E. (2014). Using graph database in spatial data generation. In D. A. Gruca, T. Czachórski, & S. Kozielski (Eds.), Man-machine interactions 3 (pp. 643–650). Springer International Publishing.
Ríos-Mercado, R. Z., & Fernández, E. (2009). A reactive GRASP for a commercial territory design problem with multiple balancing requirements. Computers & Operations Research, 36, 755–776.
Schrijver, A. (2003). Combinatorial optimization: Polyhedra and efficiency. Berlin, Heidelberg: Springer.
Sivanandam, S. N., & Deepa, S. N. (2007). Introduction to Genetic Algorithms. Berlin Heidelberg: Springer.
Zoltners, A. A., Sinha, P., & Lorimer, S. E. (2009). Building a winning sales force: Powerful strategies for driving high performance. New York: AMACOM A division of American Management Association.
Zoltners, A. A., & Sinha, P. (1983). Sales territory alignment: A review and model. Management Science, 29, 1237–1256.
Acknowledgements
This research was supported by the WIGeoGIS GmbH. We wish to thank WIGeoGIS for kindly providing the case study data. We specially thank Michael Steigemann for insightful discussions in the conception of this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sharifi Noorian, S., Murphy, C.E. (2017). Balanced Allocation of Multi-criteria Geographic Areas by a Genetic Algorithm. In: Peterson, M. (eds) Advances in Cartography and GIScience. ICACI 2017. Lecture Notes in Geoinformation and Cartography(). Springer, Cham. https://doi.org/10.1007/978-3-319-57336-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-57336-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57335-9
Online ISBN: 978-3-319-57336-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)