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Multi-period Workload Balancing in Last-Mile Urban Delivery

Yang Wang(yang-wan15***at***mails.tsinghua.edu.cn)
Lei Zhao(lzhao***at***tsinghua.edu.cn)
Martin Savelsbergh(martin.savelsbergh***at***isye.gatech.edu)
Shengnan Wu(wushengnan***at***gmail.com)

Abstract: In the daily dispatching of urban deliveries, a delivery manager has to consider workload balance among the couriers to maintain workforce morale. We consider two types of workload: incentive workload, which relates to the delivery quantity and affects a courierís income, and effort workload, which relates to the delivery time and affects a courier's health. Incentive workload has to be balanced over a long period of time (e.g., a week or a month) whereas effort workload has to be balanced over a short period of time (e.g., a shift or a day). We formulate a multi-period workload balancing problem under stochastic demand and dynamic daily dispatching as a Markov Decision Process. We propose a balanced penalty policy based on Cost Function Approximation and use a hybrid algorithm combining the modified nested partitions method and the KN++ procedure to search for the optimal policy parameters. A comprehensive numerical study demonstrates that the proposed balanced penalty policy outperforms four benchmark policies and establishes the impact of demand variation and manager preferences on workload balance.

Keywords: Incentive & effort workload, multi-period workload balancing, last-mile urban delivery, Markov decision process, cost function approximation

Category 1: Applications -- OR and Management Sciences

Category 2: Applications -- OR and Management Sciences (Transportation )

Citation: Department of Industrial Engineering, Tsinghua University & H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. Current version: January 2021

Download: [PDF]

Entry Submitted: 01/09/2021
Entry Accepted: 01/10/2021
Entry Last Modified: 01/09/2021

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