Algorithmic Mechanism Design for Collaboration in Large-Scale Transportation Networks

Minghui Lai () and Xiaoqiang Cai ()
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Minghui Lai: Southeast University
Xiaoqiang Cai: The Chinese University of Hong Kong (Shenzhen) and The Shenzhen Research Institute of Big Data

A chapter in Large Scale Optimization in Supply Chains and Smart Manufacturing, 2019, pp 257-282 from Springer

Abstract: Abstract The importance of collaborative logistics is getting widely recognized in recent years. However, strategic revealing of private information and disagreements on how savings are split would make any efforts of collaboration unsuccessful. The large-scale nature of real-life transportation networks further complicates the implementation of collaboration, as the associated optimization problems are usually NP-hard. The academic community has developed a variety of methodologies by using operations research tools and algorithmic mechanism design to resolve these issues. We summarize the state-of-the-art progress in the literature and introduce the iterative mechanism design theories. The iterative mechanism design models consider private information and propose decentralized approximation algorithms to achieve a system-wide objective while eliciting truthful information through the iterations. Hence, iterative mechanism design effectively reduces computational and communication difficulties. We then apply the methodology to a truckload pickup-and-delivery collaboration problem as an example. Numerical results on large-scale instances are reported, verifying the effectiveness of the methodologies.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-22788-3_9

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DOI: 10.1007/978-3-030-22788-3_9

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