Journal article
Same-day delivery with fair customer service
European journal of operational research, Vol.308(2), pp.738-751
07/16/2023
DOI: 10.1016/j.ejor.2022.12.009
Abstract
•We introduce the concept of customer fairness to urban delivery.•We provide a multi-objective Markov decision process model that maximizes both business utility and customer fairness.•We propose a deep Q-learning solution approach that improves fairness with a small loss of utility.•We provide a comprehensive analysis of how considering fairness changes customer experience.
The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. In 2016, due to low concentrations of memberships and far distance from the depot, certain minority neighborhoods were excluded from receiving Amazon’s SDD service, raising concerns about fairness. In this paper, we study the problem of offering fair SDD service to customers. The service area is partitioned into different regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make deliveries to accepted customers before their delivery deadline. In addition to overall service rate (utility), we maximize the minimal regional service rate across all regions (fairness). We model the problem as a multi-objective Markov decision process and develop a deep Q-learning solution approach. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learning process. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We show this effectiveness is valid with different depot locations, providing businesses with an opportunity to achieve better fairness from any location. We also show that the proposed approach performs efficiently when serving heterogeneously wealthy districts in the city.
Details
- Title: Subtitle
- Same-day delivery with fair customer service
- Creators
- Xinwei Chen - Bucknell UniversityTong Wang - Department of Business Analytics, University of Iowa, 108 Pappajohn Business Building, Iowa City, IA 52242, United StatesBarrett W. Thomas - Department of Business Analytics, University of Iowa, 108 Pappajohn Business Building, Iowa City, IA 52242, United StatesMarlin W. Ulmer - Otto von Guericke Universität Magdeburg, Universitätsplatz 2, Magdeburg 39106, Germany
- Resource Type
- Journal article
- Publication Details
- European journal of operational research, Vol.308(2), pp.738-751
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.ejor.2022.12.009
- ISSN
- 0377-2217
- eISSN
- 1872-6860
- Grant note
- DOI: 10.13039/100004807, name: California Department of Fish and Game, award: 444657906; DOI: 10.13039/501100001659, name: Deutsche Forschungsgemeinschaft
- Language
- English
- Date published
- 07/16/2023
- Academic Unit
- Bus Admin College; Business Analytics
- Record Identifier
- 9984380549102771
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