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Two metaheuristic approaches for solving the multi-compartment vehicle routing problem

Author

Listed:
  • Hiba Yahyaoui

    (Université de Tunis
    Dhofar University)

  • Islem Kaabachi

    (Université de Tunis)

  • Saoussen Krichen

    (Université de Tunis)

  • Abdulkader Dekdouk

    (Dhofar University)

Abstract

We address in this paper a multi-compartment vehicle routing problem (MCVRP) that aims to plan the delivery of different products to a set of geographically dispatched customers. The MCVRP is encountered in many industries, our research has been motivated by petrol station replenishment problem. The main objective of the delivery process is to minimize the total driving distance by the used trucks. The problem configuration is described through a prefixed set of trucks with several compartments and a set of customers with demands and prefixed delivery. Given such inputs, the minimization of the total traveled distance is subject to assignment and routing constraints that express the capacity limitations of each truck’s compartment in terms of the pathways’ restrictions. For the NP-hardness of the problem, we propose in this paper two algorithms mainly for large problem instances: an adaptive variable neighborhood search (AVNS) and a Partially Matched Crossover PMX-based Genetic Algorithm to solve this problem with the goal of ensuring a better solution quality. We compare the ability of the proposed AVNS with the exact solution using CPLEX and a set of benchmark problem instances is used to analyze the performance of the both proposed meta-heuristics.

Suggested Citation

  • Hiba Yahyaoui & Islem Kaabachi & Saoussen Krichen & Abdulkader Dekdouk, 2020. "Two metaheuristic approaches for solving the multi-compartment vehicle routing problem," Operational Research, Springer, vol. 20(4), pages 2085-2108, December.
    • Handle: RePEc:spr:operea:v:20:y:2020:i:4:d:10.1007_s12351-018-0403-4
    DOI: 10.1007/s12351-018-0403-4
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    References listed on IDEAS

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    2. Wu, Weitiao & Li, Yu, 2024. "Pareto truck fleet sizing for bike relocation with stochastic demand: Risk-averse multi-stage approximate stochastic programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).

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