Preprint
Underground Freight Transportation for Package Delivery in Urban Environments
arXiv.org
Cornell University
05/07/2024
DOI: 10.48550/arxiv.2405.04618
Abstract
The use of underground freight transportation (UFT) is gaining attention
because of its ability to quickly move freight to locations in urban areas
while reducing road traffic and the need for delivery drivers. Since packages
are transported through the tunnels by electric motors, the use of tunnels is
also environmentally friendly. Unlike other UFT projects, we examine the use of
tunnels to transport individual orders, motivated by the last mile delivery of
goods from e-commerce providers. The use of UFT for last mile delivery requires
more complex network planning than for direct lines that have previously been
considered for networks connecting large cities. We introduce a new network
design problem based on this delivery model and transform the problem into a
fixed charge multicommodity flow problem with additional constraints. We show
that this problem, the nd-UFT, is NP-hard, and provide an exact solution method
for solving large-scale instances. Our solution approach exploits the
combinatorial sub-structures of the problem in a cutting planes fashion,
significantly reducing the time to find optimal solutions on most instances
compared to a MIP. We provide computational results for real urban environments
to build a set of insights into the structure of such networks and evaluate the
benefits of such systems. We see that a budget of only 45 miles of tunnel can
remove 42% of packages off the roads in Chicago and 32% in New York City. We
estimate the fixed and operational costs for implementing UFT systems and break
them down into a per package cost. Our estimates indicate over a 40% savings
from using a UFT over traditional delivery models. This indicates that UFT
systems for last mile delivery are a promising area for future research.
Details
- Title: Subtitle
- Underground Freight Transportation for Package Delivery in Urban Environments
- Creators
- Sarah PowellAnn Melissa CampbellMojtaba Hosseini
- Resource Type
- Preprint
- Publication Details
- arXiv.org
- DOI
- 10.48550/arxiv.2405.04618
- eISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 05/07/2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984627259602771
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