An adaptive solution to dynamic transport optimization

ABSTRACT

This paper describes LS/ATN, Living Systems®Adaptive Transportation Networks, an agent-based solution we have developed to solve transportation problems in the charter business logistics. LS/ATN provides automatic optimization and execution capabilities that extend the existing planning systems accordingly. To describe our solution and analyse its performance, we report on a real case scenario in which transportation requests of a big logistics provider were optimized. Besides describing the agent approach and the LS/ATN features we stress the necessity to integrate such agent system into a real-world IT architecture. Finally, we show that our adaptive solution produces significantly better results in real case scenarios than what achieved with manual optimization of professional dispatchers.

REFERENCES

• 1: K. Fischer, J. P. Müller, and M. Pischel. Cooperative transportation scheduling: an application domain for DAI. Journal of Applied Artificial Intelligence, 10, 1995.
• 2: J.-J. Jaw, A. R. Odoni, H. N. Psaraftis, and N. H. Wilson. A heuristic algorithm for the multi-vehicle advance request dial-a-ride problem with time windows. Transportation Research, 20 B(3):243--257, 1986.
• 3: P. Klaus. TOP 100 in European Transport and Logistics Services. Deutscher Verkehrs-Verlag, 2004.
• 4: Robert Kohout , Kutluhan Erol, In-time agent-based vehicle routing with a stochastic improvement heuristic, Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, p.864-869, July 18-22, 1999, Orlando, Florida, United States
• 5: Snezana Mitrovic-Minic. Pickup and delivery problem with time windows: A survey. Technical Report TR 1998--12, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada, May 1998.
• 6: W. P. Nanry and J. W. Barnes. Solving the pickup and delivery problem with time windows using reactive tabu search. Transportation Research, 34B:107--121, 2000.
• 7: Exel News. Uk consumer products industry cites cost reduction as its biggest logistics challenge. In http://www.exel.com/exel/home/media/news/newsreleases/-pressreleasecostreduction.htm, London, UK, 2002.
• 8: H. Psaraftis. Dynamic vehicle routing: status and prospects. Annals of Operations Research, 61:143--164, 1995.
• 9: M. W. P. Savelsbergh and M. Sol. The general pickup and delivery problem. Transportation Science, 29(1):17--29, 1995.
• 10: Paul M. Thompson , Harilaos N. Psaraftis, Cyclic transfer algorithms for multivehicle routing and scheduling problems, Operations Research, v.41 n.5, p.935-946, Sept.–Oct 1993 [doi>10.1287/opre.41.5.935]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

Additional Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.2 Types of Systems

General Terms:
Algorithms, Economics

Keywords:
LS/ATN, agent system, dynamic optimization, transportation logistics