Provably Good Region Partitioning for On-Time Last-Mile Delivery

Author:

Carlsson John Gunnar1ORCID,Liu Sheng2ORCID,Salari Nooshin34,Yu Han1

Affiliation:

1. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089;

2. Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada;

3. Donadeo Innovation Center for Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada

4. DeGroote School of Business, McMaster University, Hamilton, Ontario L8S 4M4, Canada

Abstract

Managing on-time delivery systems is challenging because of the underlying uncertainties and combinatorial nature of the routing decision. In practice, the efficiency of such systems also hinges on the driver’s familiarity with the local neighborhood. In “Provably Good Region Partitioning for On-Time Last-Mile Delivery,” Carlsson et al. study a region partitioning policy to minimize the expected delivery time of customer orders in a stochastic and dynamic setting. This policy assigns every driver to a subregion, ensuring that drivers are only dispatched to their territories. The authors characterize the structure of the optimal partitioning policy and show its expected on-time performance converges to that of the flexible dispatching policy in heavy traffic. The optimal characterization features two insightful conditions that are critical to the on-time performance of last-mile delivery systems. Furthermore, the paper develops partitioning algorithms with performance guarantees, leveraging ham sandwich cuts and three-partitions from discrete geometry.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Computer Science Applications

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