A decomposition-based approach for large-scale pickup and delivery problems

TL;DR

This paper introduces a scalable algorithmic framework for large-scale pickup and delivery problems, enabling efficient routing for autonomous vehicle services with over 20,000 requests, and provides insights on fleet sizing and customer delay management.

Contribution

The paper presents a novel decomposition-based approach capable of handling extremely large-scale pickup and delivery problems, surpassing previous methods in scalability and practical applicability.

Findings

Successfully solved instances with over 20,000 requests

Demonstrated improvements in fleet sizing and customer delay acceptance

Provided comparative analysis and benchmark results

Abstract

With the advent of self-driving cars, experts envision autonomous mobility-on-demand services in the near future to cope with overloaded transportation systems in cities worldwide. Efficient operations are imperative to unlock such a system's maximum improvement potential. Existing approaches either consider a narrow planning horizon or ignore essential characteristics of the underlying problem. In this paper, we develop an algorithmic framework that allows the study of very large-scale pickup and delivery routing problems with more than 20 thousand requests, which arise in the context of integrated request pooling and vehicle-to-request dispatching. We conduct a computational study and present comparative results showing the characteristics of the developed approaches. Furthermore, we apply our algorithm to related benchmark instances from the literature to show the efficacy. Finally, we solve very large-scale instances and derive insights on upper-bound improvements regarding fleet sizing and customer delay acceptance from a practical perspective.