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A prescriptive framework to support express delivery supply chain expansions in highly urbanized environments

Rafael Diaz (VMASC, Old Dominion University, Norfolk, Virginia, USA)
Canh Phan (Viec.Co Corp, Ho Chi Minh, Vietnam)
Daniel Golenbock (Clariant Corp, Charlotte, North Carolina, USA)
Benjamin Sanford (Yak Mat, East Columbia, Mississippi, USA)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 6 June 2022

Issue publication date: 21 June 2022




With the proliferation of e-commerce companies, express delivery companies must increasingly maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a highly uncertain environment. This paper provides a framework for leveraging demand data to determine sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.


While the literature points out several hub assignment methods, the authors propose an alternative spherical-clustering algorithm for densely urbanized population environments to strengthen the accuracy and robustness of current models. The authors complement this approach with straightforward mathematical optimization and simulation models to generate and test designs that effectively align environmentally sustainable solutions.


To examine the effects of different degrees of demand variability, the authors analyzed this approach's performance by solving a real-world case study from an express delivery company's primary market. The authors structured a four-stage implementation framework to facilitate practitioners applying the proposed model.


Previous investigations explored driving distances on a spherical surface for facility location. The work considers densely urbanized population and traffic data to simultaneously capture demand patterns and other road dynamics. The inclusion of different population densities and sustainability data in current models is lacking; this paper bridges this gap by posing a novel framework that increases the accuracy of spherical-clustering methods.



The authors would like to thank the subject matter experts and anonymous reviewers for their insightful guidance and suggestions. This research was supported in part by ODU-VMASC Internal Research and Development Project 300770-010: Exploring Additional Applications of Supply Chain Management, AI, and Cybersecurity.


Diaz, R., Phan, C., Golenbock, D. and Sanford, B. (2022), "A prescriptive framework to support express delivery supply chain expansions in highly urbanized environments", Industrial Management & Data Systems, Vol. 122 No. 7, pp. 1707-1737.



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