Publication Type

Journal Article

Version

submittedVersion

Publication Date

1-2023

Abstract

This work is motivated by a real-world problem of coordinating B2B pickup-delivery operations to shopping malls involving multiple non-collaborative logistics service providers (LSPs) in a congested city where space is scarce. This problem can be categorized as a vehicle routing problem with pickup and delivery, time windows and location congestion with multiple LSPs (or ML-VRPLC in short), and we propose a scalable, decentralized, coordinated planning approach via iterative best response. We formulate the problem as a strategic game where each LSP is a self-interested agent but is willing to participate in a coordinated planning as long as there are sufficient incentives. Through an iterative best response procedure, agents adjust their schedules until no further improvement can be obtained to the resulting joint schedule. We seek to find the best joint schedule which maximizes the minimum gain achieved by any one LSP, as LSPs are interested in how much benefit they can gain rather than achieving a system optimality. We compare our approach to a centralized planning approach and our experiment results show that our approach is more scalable and is able to achieve on average 10% more gain within an operationally realistic time limit.

Keywords

Best response planning, Multi-agent systems, Vehicle routing problem

Discipline

Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Springer Nature Computer Science

Volume

4

ISSN

2661-8907

Identifier

10.1007/s42979-022-01551-w

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s42979-022-01551-w

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