Advertisement

Territorial Design Optimization for Business Sales Plan

  • Laura Hervert-Escobar
  • Vassil Alexandrov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10665)

Abstract

A well designed territory enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost. This paper considers a real life case study to design a sales territory for a business sales plan. The business plan consists in assigning the optimal quantity of sellers to a territory including the scheduling and routing plans for each seller. The problem is formulated as a combination of assignment, scheduling and routing optimization problems. The solution approach considers a meta-heuristic using stochastic iterative projection method for large systems. Several real life instances of different sizes were tested with stochastic data to represent raise/fall in the customers demand as well as the appearance/loss of customers.

Keywords

Territory design Projection methods Optimization 

References

  1. 1.
    Baase, S., Gelder, A.V.: Computer Algorithms: Introduction to Design and Analysis. Addison Wesley, Boston (1999)Google Scholar
  2. 2.
    Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., Juan, A.A.: Rich vehicle routing problem: survey. ACM Comput. Surv. 47(2), 32:1–32:28 (2014). http://doi.acm.org/10.1145/2666003 CrossRefGoogle Scholar
  3. 3.
    Current, J., Daskin, M., Schilling, D.: Discrete network location models. In: Drezner, Z., Hamacher, H.W. (eds.) Facility Location: Applications and Theory, chap. 3, pp. 81–118. Springer, Heidelberg (2002)Google Scholar
  4. 4.
    Daskin, M.S.: Network and Discrete Location: Models, Algorithms, and Applications. Wiley, New York (2013). ISBN: 978-0-470-90536-4zbMATHGoogle Scholar
  5. 5.
    Dondo, R., Cerda, J.: A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows. Eur. J. Oper. Res. 176(3), 1478–1507 (2007). http://www.sciencedirect.com/science/article/pii/S0377221705008672 CrossRefzbMATHGoogle Scholar
  6. 6.
    Drezner, Z.: Note-on a modified one-center model. Manag. Sci. 27(7), 848–851 (1981). http://dx.doi.org/10.1287/mnsc.27.7.848 CrossRefzbMATHGoogle Scholar
  7. 7.
    Sabelfeld, K., Loshchina, N.: Stochastic iterative projection methods for large linear systems. Monte Carlo Methods Appl. 16(3–4), 343–359 (2010). http://EconPapers.repec.org/RePEc:bpj:mcmeap:v:16:y:2010:i:3–4:p:343–359:n:13 MathSciNetzbMATHGoogle Scholar
  8. 8.
    Tugba, C., Emre, V.: Outpatient scheduling in health care: a review of literature. Prod. Ope. Manag. 12(4), 519–549 (2003). http://dx.doi.org/10.1111/j.1937-5956.2003.tb00218.x Google Scholar
  9. 9.
    Watson-Gandy, C.: Heuristic procedures for the m-partial cover problem on a plane. Eur. J. Oper. Res. 11(2), 149–157 (1982). http://www.sciencedirect.com/science/article/pii/0377221782901096 CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Instituto Tecnológico y de Estudios Superiores de MonterreyMonterreyMexico
  2. 2.ICREA - Catalan Institution for Research and Advanced StudiesBarcelonaSpain
  3. 3.Barcelona Supercomputing CenterBarcelonaSpain

Personalised recommendations