Interaction between intelligent agent strategies for real-time transportation planning

Research output: Working paper

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Abstract

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach.
Original languageUndefined
Place of PublicationEnschede
PublisherUniversity of Twente, Research School for Operations Management and Logistics (BETA)
Number of pages29
ISBN (Print)9789038621982
Publication statusPublished - 2010

Publication series

NameBeta working papers
PublisherBeta Research School for Operations Management and Logistics, University of Twente
No.307

Keywords

  • Vehicle
  • Distributed decision making
  • IR-70203
  • Auctions/bidding
  • Transportation
  • Multi-Agent Systems

Cite this

Mes, M. R. K., van der Heijden, M. C., & Schuur, P. (2010). Interaction between intelligent agent strategies for real-time transportation planning. (Beta working papers; No. 307). Enschede: University of Twente, Research School for Operations Management and Logistics (BETA).