Soh, N. W. Z., Zhang, A. N., Wan, F., & Xu, C. (2024, January 12). Urban Last Mile Delivery Data Mining for Performance Improvement. Proceedings of the 2024 7th International Conference on Computers in Management and Business. https://doi.org/10.1145/3647782.3647796
Abstract:
This paper presents an in-depth exploration of data mining techniques
aimed at optimizing the operational efficiency of urban last
mile delivery services. Leveraging an authentic industry dataset
graciously provided by a collaborative logistics partner, this study
meticulously unravels intricate delivery patterns and discerns clusters
attributed to delays, employing advanced cluster analysis
methodologies through the utilization of the WEKA software suite.
From our initial cluster analysis, we identified specifically that
33% of late cases occurred in the latitude range of (1.277, 1.287],
36% occurred in the longitude range of (103.843, 103.855], and that
Driver 2065 was involved in 13% of late cases. Furthermore, a
pioneering route analysis paradigm is introduced, elucidating an
implementation framework harnessed through Python, Pandas,
Folium packages, and the Open Source Routing Machine (OSRM)
API. Through our route analysis, we were able to visualize the historical
routes taken by drivers and the recommended routes by
OSRM for their given jobs. In the case of Driver 2065, this allowed
us to identify visits to non-job locations and extended durations
spent at high-rise and high-density buildings. Notably, this research
surmounts the challenge posed by imprecise GPS coordinates for
job locations by propounding an innovative approach to location
estimation. This groundbreaking technique bestows the capability
to compute pivotal parameters, encompassing travel time and service
duration, which aptly characterizes the temporal allocation at
each discrete job locale. The culmination of our scholarly pursuits
begets profound insights, effectively serving as a guiding compass
to engender tangible operational enhancements and methodical
finesse in the domain of delivery operations, thereby ensuring the
punctilious execution of time-sensitive deliveries.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the A*STAR - GAP
Grant Reference no. : I22D1AG003