Abstract
Vehicle Routing Problem is the most common and simplest routing problems. One of its important variants is the Dynamic Vehicle Routing Problem in which a new customer orders and order cancellations continually happen over time and thus perturb the optimal routing schedule that was originally invented. The Dynamic Vehicle Routing Problem is an NP-Hard problem aims to design the route set of minimum cost for a homogenous feet of vehicles, starting and terminating at the depot, to serve all the customers. In this paper, we propose a prototype of a Decision Support System that integrates a hybrid of Genetic Algorithm and Local Search to solve the Dynamic Vehicle Routing Problem. The performance of the proposed algorithm is highlighted through the implementation of the Decision Support System. Some benchmark problems are selected to test the performance of the proposed hybrid method. Our approach is better than the performance of compared algorithms in most cases in terms of solution quality and robustness. In order to demonstrate the performance of the proposed Decision Support System in term of solution quality, we apply it for a real case of the Regional Post Office of the city of Kef in the north west of Tunisia. The results are then highlighted in a cartographic format using Google Maps.
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Sbai, I., Limam, O., Krichen, S. (2018). A Decision Support System Based on a Hybrid Genetic Local Search Heuristic for Solving the Dynamic Vehicle Routing Problem: Tunisian Case. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_30
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DOI: https://doi.org/10.1007/978-3-319-91479-4_30
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