This report addresses the capacitated vehicle routing problem (CVRP) and the split delivery vehicle routing problem (SDVRP) with uncertain travel times and demands when planning vehicle routes for delivering critical supplies to the affected population in need after a disaster. A robust optimization approach is used to formulate the CVRP and the SDVRP with uncertain travel times and demands for five objective functions: minimization of the total number of vehicles deployed (minV), minimization of the total travel times/travel costs (minT), minimization of the summation of arrival times (minS), minimization of the summation of demand-weighted arrival times (minD), and minimization of the latest arrival time (minL). The minS, minD, and minL are critical for deliveries to be fast and fair in routing for relief efforts, while the minV and minT are common cost-based objective functions in the traditional VRP. A two-stage heuristic method that combines the insertion algorithm and tabu search is used to solve the VRP models for large-scale problems. The solutions of the CVRP and the SDVRP are compared for different examples. Keywords: Robust Optimization, Vehicle Routing Problem, Tabu Search, Insertion Algorithm


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    Title :

    Disaster Relief Routing Under Uncertainty: a Robust Optimization Approach


    Contributors:
    S. H. Chung (author) / Y. Li (author)

    Publication date :

    2017


    Size :

    44 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




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