Abstract Dynamic routing problem with crowd-sourced driver can be challenging as the dynamically arrived crowd drivers are utilized to meet the dynamically placed customer demand. To guarantee a certain service level, the company also operates a set of employed vehicles as a backup capacity to serve the tasks for which the use of a crowd driver is not feasible or not efficient. Thus, in this paper, we study a special dynamic routing problem that arises in such an environment, in which a mix of company vehicles and dynamically arrived crowd drivers serves dynamically placed orders. The company seeks route policies for crowd drivers and company vehicles so as to minimize the total costs. We present a hierarchical reinforcement learning method that combines upper-level agent for balancing the opportunities and risks brought by delayed batch-matching and lower-level agents for route planning. The effectiveness of our approach is demonstrated through extensive numerical analyses, with the results showing the benefits derived from the upper-level agent and the designed dual-decoder architecture of lower-level agent. The solution quality, computation time, and generalization ability of the proposed approach are all proven to be superior over existing methods.

    Highlights Dynamic crowd logistics problem with delayed batch matching is presented. Hierarchical reinforcement learning method is proposed for solving DCLP. Dual-decoder attention model is proposed and integrated in HRL. The proposed models are verified through comparisons with classic method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dual-decoder attention model in hierarchical reinforcement framework for dynamic crowd logistics problem with batch-matching


    Contributors:
    Xiang, Chuankai (author) / Wu, Zhibin (author) / Zhou, Yufeng (author) / Tu, Jiancheng (author)


    Publication date :

    2023-11-11




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    A Crowd Counting Framework Combining with Crowd Location

    Jin Zhang / Sheng Chen / Sen Tian et al. | DOAJ | 2021

    Free access

    A Hierarchical Framework for Modal Correspondence Matching

    Carcassoni, M. / Hancock, E. / IEEE | British Library Conference Proceedings | 2001


    A hierarchical framework for modal correspondence matching

    Carcassoni, M. / Hancock, E.R. | IEEE | 2001


    A Microscopic-To-Macroscopic Crowd Dynamic Model

    Al-nasur, S. / Kachroo, P. | IEEE | 2006