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A multi-agent and auction-based framework and approach for carrier collaboration

Bo Dai , Haoxun Chen

Original Paper

First online: 11.02.2011

DOI: 10.1007/s12159-011-0046-9

Cite this article as: Dai, B. & Chen, H. Logist. Res. (2011) 3: 101. doi:10.1007/s12159-011-0046-9

Abstract

Carrier collaboration in transportation means multiple carriers form an alliance to optimize their transportation operations through sharing transportation requests and vehicle capacities. In this paper, we propose a multi-agent and auction-based framework and approach for carrier collaboration in less than truckload transportation. In this framework, the carriers outsource/acquire requests through multiple auctions, one for outsourcing each request; a carrier acts as an auctioneer when it wants to outsource a request to other carriers, whereas the carrier acts as a bidder when it wants to acquire a request from other carriers; for each carrier, which requests it should outsource and acquire are determined by solving its outsourcing requests selection problem and requests bidding problem, respectively. These two decision problems are formulated as mixed integer programming problems. The auction of each request is multiround; in each round, the auctioneer determines the outsourcing price of the request and each bidder determines whether it acquires the request at the given price; the auctioneer lowers the outsourcing price if multiple carriers bid for the request or raises the price if no carrier bids for it. The auction process continues until only one carrier bids for the request or a given number of rounds are achieved. In the second case, if no agent bids for the request, then it is returned to the outsourcing agent; if multiple bidding agents compete for the request, a conflict resolution procedure is used to determine which carrier wins it. The approach is decentralized, asynchronous, and dynamic, where multiple auctions may occur simultaneously and interact with each other. The performance of the approach is evaluated by randomly generated instances and compared with an individual planning approach and a centralized planning approach.

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