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An Optimal Algorithm for Sales Representative Time Management

Author

Listed:
  • Andris A. Zoltners

    (Northwestern University)

  • Prabhakant Sinha

    (University of Georgia)

  • Philip S. C. Chong

    (North Dakota State University)

Abstract

This paper addresses the time management problem confronted by sales representatives. The sales representative planning his itinerary must decide the best way to ration time among the accounts comprising his territory. The time management problem is formulated as an integer program whereby each admissible call frequency for each account is represented by a zero-one decision variable. A branch-and-bound integer programming algorithm for this problem is presented. The algorithm is unique in that two integer programming formulations of the problem are used simultaneously in the search procedure and an approximation-cum-relaxation is evaluated at each branch in the search. Computational testing of the algorithm shows that it can solve many realistic time management problems optimally in fractions of a second.

Suggested Citation

  • Andris A. Zoltners & Prabhakant Sinha & Philip S. C. Chong, 1979. "An Optimal Algorithm for Sales Representative Time Management," Management Science, INFORMS, vol. 25(12), pages 1197-1207, December.
    • Handle: RePEc:inm:ormnsc:v:25:y:1979:i:12:p:1197-1207
    DOI: 10.1287/mnsc.25.12.1197
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    Cited by:

    1. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    2. AgralI, Semra & Geunes, Joseph, 2009. "Solving knapsack problems with S-curve return functions," European Journal of Operational Research, Elsevier, vol. 193(2), pages 605-615, March.
    3. Amin Sayedi & Jeffrey D. Shulman, 2017. "Strategic compliments in sales," Quantitative Marketing and Economics (QME), Springer, vol. 15(1), pages 57-84, March.
    4. Andreas Drexl & Knut Haase, 1999. "Fast Approximation Methods for Sales Force Deployment," Management Science, INFORMS, vol. 45(10), pages 1307-1323, October.
    5. Lucas Javaudin & Andrea Araldo & André de Palma, 2021. "Large-Scale Allocation of Personalized Incentives," THEMA Working Papers 2021-08, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

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