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European Journal of Operational Research







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With a growing number of services provided at or to a customer’s home, the familiarity of the service provider, or driver, with the customer’s location is increasingly important. One prominent example is retail distribution, where familiarity with the delivery location can save the driver time. In contrast to other kinds of familiarity (e.g., tasks, customer needs) that continually increase with a larger number of repetitions, location familiarity is primarily established with a driver’s first visit. Thus, familiarity results from operational routing decisions. However, as we show in this paper, there is potential value in considering the tactical value of familiarity and its development over a longer horizon. To this end, we develop and solve a tactical model to specify the long-term implications of improved driver-customer familiarity, introducing a solution methodology for the stochastic and dynamic multi-period routing problem with driver-customer familiarity. Our methodology utilizes a policy that explicitly invests in the familiarity between selected driver-customer pairs, encouraging the development of pairs that are tactically beneficial. We determine the appropriate investment dimensions for each pair, considering which locations a driver has visited and how many drivers have visited a location. We show that under the problem conditions tested this investment policy leads to a reduction in cost compared to a short term, myopic policy, while increasing the overall level of familiarity between drivers and customers and hedging against driver or customer turnover. We also find that focusing only on routing or on exploiting existing familiarity leads to substantial increases in cost.


Author Posting © Elsevier, 2020. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in European Journal of Operational Research, Volume 286, Issue 2, October 2020.

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Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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