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

Author

Listed:
  • Hiba Yahyaoui

    (ISG, Tunis, Tunisia)

  • Saoussen Krichen

    (ISG, Tunis, Tunisia)

  • Abdelkader Dekdouk

    (Dhofar University, Salalah, Oman)

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.

Suggested Citation

  • Hiba Yahyaoui & Saoussen Krichen & Abdelkader Dekdouk, 2018. "A Decision Model Based on a GRASP Genetic Algorithm for Solving the Vehicle Routing Problem," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 9(2), pages 72-90, April.
    • Handle: RePEc:igg:jamc00:v:9:y:2018:i:2:p:72-90
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    Cited by:

    1. Marwa Koubaa & Mohamed Haykal Ammar & Noura Beji, 2022. "Solving a Real Case of Seafaring Staff Scheduling Problem Using Cuckoo Optimization Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-19, January.

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