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Interaction between intelligent agent strategies for real-time transportation planning

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  • Martijn Mes
  • Matthieu Heijden
  • Peter Schuur

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 different collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents offer jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a 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 strategies instead of a myopic strategy (10–20%) and (ii) the joint effect of two look-ahead strategies is larger than the effect of an individual strategy. To provide an indication of the savings that might be realized under centralized decision making, we benchmark our results against an integer programming approach. Copyright The Author(s) 2013

Suggested Citation

  • Martijn Mes & Matthieu Heijden & Peter Schuur, 2013. "Interaction between intelligent agent strategies for real-time transportation planning," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(2), pages 337-358, March.
    • Handle: RePEc:spr:cejnor:v:21:y:2013:i:2:p:337-358
    DOI: 10.1007/s10100-011-0230-7
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    Cited by:

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    2. Kong, Xiang T.R. & Kang, Kai & Zhong, Ray Y. & Luo, Hao & Xu, Su Xiu, 2021. "Cyber physical system-enabled on-demand logistics trading," International Journal of Production Economics, Elsevier, vol. 233(C).
    3. Margaretha Gansterer & Richard F. Hartl, 2021. "The Prisoners’ Dilemma in collaborative carriers’ request selection," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 73-87, March.
    4. Kuyzu, Gültekin & Akyol, Çağla Gül & Ergun, Özlem & Savelsbergh, Martin, 2015. "Bid price optimization for truckload carriers in simultaneous transportation procurement auctions," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 34-58.
    5. Los, Johan & Schulte, Frederik & Spaan, Matthijs T.J. & Negenborn, Rudy R., 2020. "The value of information sharing for platform-based collaborative vehicle routing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    6. van Lon, Rinde R.S. & Ferrante, Eliseo & Turgut, Ali E. & Wenseleers, Tom & Vanden Berghe, Greet & Holvoet, Tom, 2016. "Measures of dynamism and urgency in logistics," European Journal of Operational Research, Elsevier, vol. 253(3), pages 614-624.
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    8. Hoang, Lê Nguyên & Soumis, François & Zaccour, Georges, 2019. "The return function: A new computable perspective on Bayesian–Nash equilibria," European Journal of Operational Research, Elsevier, vol. 279(2), pages 471-485.

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