An Improved Evolutionary Algorithm for Dynamic Vehicle Routing Problem with Time Windows
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Solving Dial-a-Ride Problems with a Low-Level Hybridization of Ants and Constraint Programming
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
A Hybrid Algorithm for Vehicle Routing Problem with Time Windows
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem
Expert Systems with Applications: An International Journal
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The Vehicle Routing Problem with Time Windows (VRPTW) is a well-known and complex combinatorial problem, which has received considerable attention in recent years. Results from exact methods have been improved exploring parallel implementations and modern branch-and-cut techniques. However, 23 out of the 56 high order instances from Solomon's test set still remain unsolved. Additionally, in many cases a prohibitive time is needed to find the exact solution. Many efficient heuristic methods have been developed to make possible a good solution in a reasonable amount of time. Using travel distance as the main objective, this paper proposes a robust heuristic approach for the VRPTW using an efficient genetic algorithm and a set partitioning formulation. The tests were produced using both, real numbers and truncated data type, making it possible to compare the results with previous heuristic and exact methods published. Furthermore, computational results show that the proposed heuristic approach outperforms all previous known heuristic methods in the literature, in terms of the minimal travel distance.