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Multi-Period Workload Balancing in Last-Mile Urban Delivery

In the daily dispatching of last-mile urban delivery, a delivery manager has to consider workload balance among couriers to maintain workforce morale. We consider two types of workload: incentive workload, which relates to the delivery quantity and affects a courier’s income, and effort workload, which relates to the delivery time and affects a courier’s health. Incentive workload has to be balanced over a relatively long period of time (a payroll cycle—a week or a month), whereas effort workload has to be balanced over a relatively short period of time (a shift or a day). We introduce a multi-period workload balancing problem under stochastic demand and dynamic daily dispatching, formulate it as a Markov decision process (MDP), and derive a lower bound on the optimal value of the MDP model. 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 an optimal policy parameter. A comprehensive numerical study demonstrates that the proposed balanced penalty policy performs close to optimal on small instances and outperforms four benchmark policies on large instances, and provides insight into the impact of demand variation and a manager’s importance weighting of operating cost and workload balance.

Ketersediaan

Informasi Detail

Judul Seri

TRANSPORTATION SCIENCE; Vol.56 No.5 September-October 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1348-1368

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MARKOV DECISION PROCESS
INCENTIVE AND EFFORT WORKLOAD
MULTI-PERIOD WORKLOAD BALANCING
LAST-MILE URBAN DELIVERY
COST FUNCTION APPROXIMATION

Info Detail Spesifik

https://doi.org/10.1287/trsc.2022.1132

Pernyataan Tanggungjawab

Yang Wang, Lei Zhao, Martin Savelsbergh, Shengnan Wu

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