The vehicle routing problem
Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows
Journal of Heuristics
Sequential search and its application to vehicle-routing problems
Computers and Operations Research
A general heuristic for vehicle routing problems
Computers and Operations Research
The vehicle routing problem with flexible time windows and traveling times
Discrete Applied Mathematics - Special issue: Discrete algorithms and optimization, in honor of professor Toshihide Ibaraki at his retirement from Kyoto University
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Subset-Row Inequalities Applied to the Vehicle-Routing Problem with Time Windows
Operations Research
Agents towards vehicle routing problems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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The vehicle routing problem with time windows (VRPTW) has been the subject of intensive study because of its importance in real applications. In this paper, we propose a cross entropy multiagent learning algorithm, which considers an optimum solution as a rare event to be learned. The routing policy is node-distributed, controlled by a set of parameterized probability distribution functions. Based on the performance of experienced tours of vehicle agents, these parameters are updated iteratively by minimizing Kullback-Leibler cross entropy in order to generate better solutions in next iterations. When applying the proposed algorithm on Solomon's 100-customer problem set, it shows outperforming results in comparison with the classical cross entropy approach. Moreover, this method needs only very small number of parameter settings. Its implementation is also relatively simple and flexible to solve other vehicle routing problems under various dynamic scenarios.