Highlights We introduce three versions of Periodic-Location-Routing Problems (PLRPs). We develop a Unified-Adaptive Large Neighborhood Search (U-ALNS) metaheuristic. The U-ALNS outperforms all previous algorithms on the PLRP. Results on new benchmark instances for PLRPs are presented.

    Abstract This paper introduces three variants of the Periodic Location-Routing Problem (PLRP): the Heterogeneous PLRP with Time Windows (HPTW), the Heterogeneous PLRP (HP) and the homogeneous PLRP with Time Windows (PTW). These problems extend the well-known location-routing problem by considering a homogeneous or heterogeneous fleet, multiple periods and time windows. The paper develops a powerful Unified-Adaptive Large Neighborhood Search (U-ALNS) metaheuristic for these problems. The U-ALNS successfully uses existing algorithmic procedures and also offers a number of new advanced efficient procedures capable of handling a multi-period horizon, fleet composition and location decisions. Computational experiments on benchmark instances show that the U-ALNS is highly effective on PLRPs. The U-ALNS outperforms previous methods on a set of standard benchmark instances for the PLRP. We also present new benchmark results for the PLRP, HPTW, HP and PTW.


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    Title :

    A unified-adaptive large neighborhood search metaheuristic for periodic location-routing problems


    Contributors:


    Publication date :

    2016-04-18


    Size :

    20 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English