Situation Patterns in Multi-Agent Systems for Solving Transportation Problems

The aim of the work is to propose algorithms which solve transportation problems, viz. Pickup and Delivery Problem with Time Windows (PDPTW), taking into consideration the identification and description of the current situation. The essential element of a solution is to calculate measures of the current situation and use them to decide on versions and configurations of algorithms performed dealing with given kinds of problems the best and limit the computational time.

[1]  Andrew Lim,et al.  A metaheuristic for the pickup and delivery problem with time windows , 2001, Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001.

[2]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[3]  Jaroslaw Kozlak,et al.  Multi-agent Crisis Management in Transport Domain , 2009, ICCS.

[4]  David Meignan,et al.  A Coalition-Based Metaheuristic for the vehicle routing problem , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[5]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[6]  Jörg P. Müller,et al.  COOPERATIVE TRANSPORTATION SCHEDULING : AN APPLICATION DOMAIN FOR DAI , 1996 .

[7]  Jaroslaw Kozlak,et al.  Application of Holonic Approach for Transportation Modelling and Optimising , 2011, PAAMS.

[8]  Winfried Hochstättler,et al.  The Simulated Trading Heuristic for Solving Vehicle Routing Problems , 1996, Discret. Appl. Math..

[9]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.