HighlightsThis paper addresses managing material donations in disaster relief supply chains.Both supply and demand uncertainty are considered within the context of last-mile distribution.We find optimal vehicle dispatching policies using dynamic programming.We examine the optimality gaps of two practical heuristic policies.Continuously dispatching the vehicle performs well or optimally under most conditions.

    AbstractRelief distribution has received considerable attention in the disaster operations management literature. However, the majority of this literature assumes that supply is always available. In reality, a significant portion of the materials that flow through the humanitarian relief chain are donations, which represent an uncertain supply source in terms of both quantity and timing. This paper investigates a two-stage relief chain consisting of a single staging area (SA) where donations arrive over time in uncertain quantities, which are periodically distributed to random numbers of disaster survivors located at a point of distribution (POD). A single vehicle travels back and forth between the SA and POD transporting relief supplies during a finite horizon. The goal of this study is to identify dispatching policies for the vehicle with the sole purpose of minimizing unsatisfied demand at the POD. To this end, we examine the effectiveness of two common-sense heuristic policies relative to the optimal dispatching policy, the latter of which is determined via stochastic dynamic programming. Our findings indicate that although continuously dispatching the vehicle between the SA and POD is not an optimal policy, it is either optimal or close to optimal in most situations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Dispatching policies for last-mile distribution with stochastic supply and demand


    Beteiligte:


    Erscheinungsdatum :

    2017-08-16


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Assessing last-mile distribution resilience under demand disruptions

    Pahwa, Anmol / Jaller, Miguel | Elsevier | 2023


    A Method for “Last mile” Distribution Demand for Drones

    Fang, Zhang / Hong-Hai, Zhang | IEEE | 2020


    On-demand last-mile distribution network design with omnichannel inventory

    Snoeck, André / Winkenbach, Matthias / Fransoo, Jan C. | Elsevier | 2023


    Deep reinforcement learning for stochastic last-mile delivery with crowdshipping

    Marco Silva / João Pedro Pedroso / Ana Viana | DOAJ | 2023

    Freier Zugriff

    RGMARL: Vehicle Dispatching Based on Road Information and Supply-Demand Distribution

    Hu, Xinghong / Wang, Qiang / Zhang, Wenqi et al. | IEEE | 2023