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Heuristics Algorithms for a Heterogeneous Fleets VRP with Excessive Demand for the Vehicle at the Pickup Points, and the Longest Traveling Time Constraint: A Case Study in Prasitsuksa Songkloe, Ubonratchathani Thailand

Author

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  • Sasitorn Kaewman

    (Department of Computer Science, Faculty of informatics, Mahasarakham University, Maha Sarakham 44000, Thailand)

  • Raknoi Akararungruangkul

    (Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

This paper presents a methodology to solve a special case of the vehicle routing problem (VRP) called the heterogeneous fleets VRP with excessive demand of the vehicle at the pickup points, and the longest time constraint (HFVRP-EXDE-LTC). We developed two metaheuristics—a differential evolution (DE) algorithm and an adaptive large neighborhood search (ALNS)—to solve the problem. These two proposed methods have been designed to effectively solve a special case of VRP. From the computational results, we can see that the proposed heuristics outperformed the best practices that are currently in use. The DE yielded a 9.78% lower cost than that of the current practice (757,250 baht per year), while ALNS generated a 10.89% (906,750 baht per year) lower cost than that of current practice. Comparing the proposed heuristics, ALNS achieved a 1.01% lower cost than that of DE, as ALNS had a better mechanism that was designed to escape from the local optimal.

Suggested Citation

  • Sasitorn Kaewman & Raknoi Akararungruangkul, 2018. "Heuristics Algorithms for a Heterogeneous Fleets VRP with Excessive Demand for the Vehicle at the Pickup Points, and the Longest Traveling Time Constraint: A Case Study in Prasitsuksa Songkloe, Ubonra," Logistics, MDPI, vol. 2(3), pages 1-16, August.
    • Handle: RePEc:gam:jlogis:v:2:y:2018:i:3:p:15-:d:166197
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    1. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    2. Tan, K.C. & Chew, Y.H. & Lee, L.H., 2006. "A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 172(3), pages 855-885, August.
    3. Aksen, Deniz & Kaya, Onur & Sibel Salman, F. & Tüncel, Özge, 2014. "An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem," European Journal of Operational Research, Elsevier, vol. 239(2), pages 413-426.
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