4.7 Article

Efficient auctions for distributed transportation procurement

期刊

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trb.2014.03.005

关键词

Distributed transportation procurement; Mechanism design; Efficient auctions; Primal-dual algorithm; Incentive compatibility

资金

  1. HKSAR RGC GRF Project [712513]
  2. National Natural Science Foundation of China [51305376]
  3. The University of Hong Kong Small Project Fund [201209176052, 201309176013]
  4. Zhejiang Provincial government
  5. Hangzhou Municipal government
  6. Lin'an City government

向作者/读者索取更多资源

The purpose of this paper is to propose allocatively efficient auction mechanisms for the distributed transportation procurement problem (DTPP), which is generally the problem of matching demands and supplies over a transportation network. We first construct a one-sided Vickrey-Clarke-Groves (O-VCG) combinatorial auction for the DTPP where carriers are allowed to bid on bundles of lanes. The O-VCG auction minimizes the total transportation cost (i.e., allocative efficiency) and induces truthful bidding from carriers (i.e., incentive compatibility). To simplify the execution of auction, we next propose a primal-dual Vickrey (PDV) auction based on insights from the known Ausubel auctions and the primal-dual algorithm. The PDV auction is actually a multi-round descending auction that seems simple enough for bidders. The PDV auction realizes VCG payments and truthful bidding under the condition of seller-submodularity, which implies that the effect of each individual carrier is decreasing when the coalition increases. Such is the case for the DTPP in an oversupplied transportation market. The winner determination problem of O-VCG auction is solved by the proposed primal-dual algorithm when seller-submodularity holds. Finally, carriers may reveal less private information in the PDV auction due to its dynamic procedures. (C) 2014 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据