Journal article
A priori orienteering with time windows and stochastic wait times at customers
European Journal of Operational Research, Vol.239(1), pp.70-79
11/16/2014
DOI: 10.1016/j.ejor.2014.04.040
Abstract
In the pharmaceutical industry, sales representatives visit doctors to inform them of their products and encourage them to become an active prescriber. On a daily basis, pharmaceutical sales representatives must decide which doctors to visit and the order to visit them. This situation motivates a problem we more generally refer to as a stochastic orienteering problem with time windows (SOPTW), in which a time window is associated with each customer and an uncertain wait time at a customer results from a queue of competing sales representatives. We develop a priori routes with the objective of maximizing expected sales. We operationalize the sales representative's execution of the a priori route with relevant recourse actions and derive an analytical formula to compute the expected sales from an a priori tour. We tailor a variable neighborhood search heuristic to solve the problem. We demonstrate the value of modeling uncertainty by comparing the solutions to our model to solutions of a deterministic version using expected values of the associated random variables. We also compute an empirical upper bound on our solutions by solving deterministic instances corresponding to perfect information. © 2014 Elsevier B.V. All rights reserved.
Details
- Title: Subtitle
- A priori orienteering with time windows and stochastic wait times at customers
- Creators
- Shu Zhang - University of Iowa, Business AnalyticsJeffrey W Ohlmann - University of Iowa, Business AnalyticsBarrett W Thomas - University of Iowa, Business Analytics
- Resource Type
- Journal article
- Publication Details
- European Journal of Operational Research, Vol.239(1), pp.70-79
- Publisher
- Elsevier B.V; AMSTERDAM
- DOI
- 10.1016/j.ejor.2014.04.040
- ISSN
- 0377-2217
- eISSN
- 1872-6860
- Number of pages
- 10 pages
- Language
- English
- Date published
- 11/16/2014
- Description audience
- Trade; Academic
- Academic Unit
- Business Analytics
- Record Identifier
- 9983561293702771
Metrics
146 Record Views