An adaptive agent-based approach for instant delivery order dispatching: Incorporating task buffering and dynamic batching strategies  

作　　者：Miaojia Lu Xinyu Yan Shadi Sharif Azadeh Pengling Wang 

机构地区：[1]College of Transportation Engineering,Tongji University,China [2]The Key Laboratory of Road and Traffic Engineering,Ministry of Education,4800 Cao’an Road,Shanghai 201804,China [3]Department of Civil and Environmental Engineering,Hong Kong Polytechnic University,11 Yuk Choi Rd,Hung Hom,Kowloon 999077,Hong Kong,China [4]Transport&Planning Department,Civil Engineering and Geosciences,Delft University of Technology,Mekelweg 5,Delft,South Holland 2628,Netherlands

出　　处：《International Journal of Transportation Science and Technology》2024年第1期137-154,共18页交通科学与技术(英文)

基　　金：This work was supported in part by the National Natural Science Foundation of China[72101188];the Shanghai Municipal Science and Technology Major Project[2021SHZDZX0100];the Fundamental Research Funds for the Central Universities.

摘　　要：The volume of instant delivery has witnessed a significant growth in recent years.Given the involvement of numerous heterogeneous stakeholders,instant delivery operations are inherently characterized by dynamics and uncertainties.This study introduces two order dispatching strategies,namely task buffering and dynamic batching,as potential solutions to address these challenges.The task buffering strategy aims to optimize the assignment timing of orders to couriers,thereby mitigating demand uncertainties.On the other hand,the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery dis tances.To model the instant delivery problem and evaluate the performances of order dis patching strategies,Adaptive Agent-Based Order Dispatching(ABOD)approach is developed,which combines agent-based modelling,deep reinforcement learning,and the Kuhn-Munkres algorithm.The ABOD effectively captures the system’s uncertainties and heterogeneity,facilitating stakeholders learning in novel scenarios and enabling adap tive task buffering and dynamic batching decision-makings.The efficacy of the ABOD approach is verified through both synthetic and real-world case studies.Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction,up to 275.42%,while simultaneously reducing the deliv ery distance by 11.38%compared to baseline policies.Additionally,the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios.As a result,this approach offers valuable sup port to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.

关 键 词：Instant delivery Task buffering Dynamic batching Agent-based modelling Deep reinforcement learning 

分 类 号：H31[语言文字—英语]