The vehicle routing problem
An Agent-Oriented Approach for the Dynamic Vehicle Routing Problem
IWAISE '08 Proceedings of the 2008 International Workshop on Advanced Information Systems for Enterprises
The tight bound of first fit decreasing bin-packing algorithm is FFD(I) ≤ 11/9OPT(I) + 6/9
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Cooperative agent navigation in partially unknown urban environments
Proceedings of the 3rd International Symposium on Practical Cognitive Agents and Robots
iCoMAS: an agent-based system for cooperative transportation planning in the food industry
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
Ground tactical mission support by multi-agent control of UAV operations
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
A cross entropy multiagent learning algorithm for solving vehicle routing problems with time windows
ICCL'11 Proceedings of the Second international conference on Computational logistics
Simulating UAV Surveillance for Analyzing Impact of Commitments in Multi-Agent Systems
International Journal of Agent Technologies and Systems
Deployment of multi-agent algorithms for tactical operations on UAV hardware
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Multi-agent Infrastructure Assisting Navigation for First Responders
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
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A multi-agent VRP solver is presented in this paper. It utilizes the contract-net protocol based allocation and several improvement strategies. It provides the solution with the quality of 81% compared to the optimal solution on 115 benchmark instances in polynomial time. The self-organizing capability of the system successfully minimizes the number of vehicles used. The presented solver architecture supports great runtime parallelization with incremental increase of solution quality. The presented solver demonstrates applicability to the VRP problem and easy adaptation to problem variants.