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A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem

A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem

Hiba Yahyaoui, Saoussen Krichen, Abdelkader Dekdouk
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 19
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522544531|DOI: 10.4018/IJAMC.2018040104
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MLA

Yahyaoui, Hiba, et al. "A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem." IJAMC vol.9, no.2 2018: pp.72-90. http://doi.org/10.4018/IJAMC.2018040104

APA

Yahyaoui, H., Krichen, S., & Dekdouk, A. (2018). A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 9(2), 72-90. http://doi.org/10.4018/IJAMC.2018040104

Chicago

Yahyaoui, Hiba, Saoussen Krichen, and Abdelkader Dekdouk. "A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 9, no.2: 72-90. http://doi.org/10.4018/IJAMC.2018040104

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Abstract

In this paper, the authors address a delivery process with time requirements in the supply chain, stated as follows: orders launched from customers are centralized and assigned to firms' depots for the delivery process. The consideration of a depot and a set of customers belonging to different firms, is seen as a VRPTW that serves n customers using a subset of vehicles. Implemented in this article is a DSS that handles the delivering activity in the supply chain. The DSS embeds a Greedy Randomized Adaptive Search Procedure (GRASP) and Genetic components for generating promising solutions in a concurrently run time. Simulation results are conducted on Solomon's benchmarks. The DSS records very competitive results regarding state-of-the-art approaches.

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