Enhancing Efficiency and Sustainability through Reconfigurable Delivery Systems

Funded by the Grantham Centre for Sustainable Futures, this research
explores the development of a dynamic system for urban last-mile delivery,
aimed at enhancing efficiency, resilience, and sustainability in
urban logistics. Focusing on integrating real-time traffic data and predictive
analytics, the study extends a previous work on a consolidation-based
multi-modal delivery model, grounded in Life Cycle Assessment
methodology.
Central to this research is the incorporation of real-time traffic data
from platforms like Waze. This data is crucial for dynamically reconfiguring
delivery routes in response to changing traffic conditions,
thereby maintaining operational efficiency and resilience. Alongside,
predictive analytics, utilising machine learning algorithms and demand
forecasting models, plays a significant role. Focused on Sheffield area,
the study also leverages data from platforms like UK Open Data for informed
and automated territory reconfiguration processes. We further
use Global Mapper software and clustering algorithms for dynamic adjustment
of delivery territories.
The implementation strategy included a comprehensive simulation
phase using AnyLogic. This phase was instrumental in modelling urban
traffic and delivery scenarios, allowing for an effective comparison
of the dynamic system against traditional static models. Key performance
indicators such as operational costs, delivery times, and environmental
impacts were analysed, providing valuable insights.