In-time agent-based vehicle routing with a stochastic improvement heuristic
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
An agent architecture for vehicle routing problems
Proceedings of the 2001 ACM symposium on Applied computing
The vehicle routing problem
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
A Multi-Start Simulated Annealing Algorithm for the Vehicle Routing Problem with Time Windows
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
A general heuristic for vehicle routing problems
Computers and Operations Research
Active guided evolution strategies for large-scale vehicle routing problems with time windows
Computers and Operations Research
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Subset-Row Inequalities Applied to the Vehicle-Routing Problem with Time Windows
Operations Research
A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows
Computers and Operations Research
Parallel simulated annealing for the vehicle routing problem with time windows
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
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Online vehicle routing problems with time windows are highly complex problems for which different artificial intelligence techniques have been used. In these problems, the exclusive optimization of the conventional criteria (number of vehicles and total traveled distance) leads to the appearance of geographic areas and/or time periods that are not covered by any vehicle because of their low population density. The transportation demands in these zones either cannot be satisfied or need to mobilize new vehicles. We propose two agent-oriented models that propose a particular dynamic organization of the vehicles, with the objective to minimize the appearance of such areas. The first model relies on a spatial representation of the agents' action zones, and the second model is grounded on the space-time representation of these zones. These representations are capable of maintaining an equilibrated distribution of the vehicles on the transportation network. In this paper, we experimentally show that these two means of distributing vehicles over the network provide better results than traditional insertion heuristics. They allow the agents to take their decisions while anticipating future changes in the environment.