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A tabu search algorithm for the vehicle routing problem
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Robotics and Computer-Integrated Manufacturing
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A hybrid genetic algorithm that optimizes capacitated vehicle routing problems
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A new hybrid ant colony optimization algorithm for the vehicle routing problem
Pattern Recognition Letters
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Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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A hybrid algorithm for the vehicle routing problem
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Multiple Phase Neighborhood Search-GRASP for the Capacitated Vehicle Routing Problem
Expert Systems with Applications: An International Journal
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Applying a hybrid ant colony system to the vehicle routing problem
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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Expert Systems with Applications: An International Journal
An ant colony algorithm for the multi-compartment vehicle routing problem
Applied Soft Computing
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This study considers the application of a genetic algorithm (GA) to the basic vehicle routing problem (VRP), in which customers of known demand are supplied from a single depot. Vehicles are subject to a weight limit and, in some cases, to a limit on the distance travelled. Only one vehicle is allowed to supply each customer.The best known results for benchmark VRPs have been obtained using tabu search or simulated annealing. GAs have seen widespread application to various combinatorial optimisation problems, including certain types of vehicle routing problem, especially where time windows are included. However, they do not appear to have made a great impact so far on the VRP as described here. In this paper, computational results are given for the pure GA which is put forward. Further results are given using a hybrid of this GA with neighbourhood search methods, showing that this approach is competitive with tabu search and simulated annealing in terms of solution time and quality.