We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.The DRL approach is compared against other optimization under uncertainty approaches, namely, sample-average approximation (SAA) and distributionally robust optimization (DRO). The results show the effectiveness of the DRL approach by examining out-of-sample performance.


    Access

    Download


    Export, share and cite



    Title :

    Deep reinforcement learning for stochastic last-mile delivery with crowdshipping


    Contributors:


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Potential last-mile impacts of crowdshipping services: a simulation-based evaluation

    Simoni, Michele D. / Marcucci, Edoardo / Gatta, Valerio et al. | Online Contents | 2019


    Potential last-mile impacts of crowdshipping services: a simulation-based evaluation

    Simoni, Michele D. / Marcucci, Edoardo / Gatta, Valerio et al. | Online Contents | 2019


    Crowdshipping: An open VRP variant with stochastic destinations

    Torres, Fabian / Gendreau, Michel / Rei, Walter | Elsevier | 2022