Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable City Logistics Strategies

In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. As a result, there has been a continuous growth in last mile operations, especially deliveries to residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. This project aims to develop an analytical framework to model last mile operations based on continuous approximation techniques. The model will help estimate the economic and environmental impacts of residential deliveries, from a growth perspective, and through comparative analyses between consumer decisions (e.g., trip complementarity and substitution, trip‐induced demand). The model will estimate impacts for freight operators (shipper, and carriers), and the community. Based on data from the National Household Travel Survey, and the American Time Use Survey, the researchers will conduct empirical analyses with the modeling framework. Moreover, to contend with the transportation issues, the team will evaluate a number of scenarios involving city logistics strategies such as the introduction of cargo consolidation facilities (CF), alternative delivery points, and the use of cargo bikes and zero emission vehicles for the last mile.

Methods
The uploaded dataset is an Analytical Tool (.xlsm format) to model last-mile delivery and different city logistics measures in the context of e-commcer delivery.

The objective here is to build an economic model for a last mile delivery service provider, serving N customers in a service region of size A in nr periods of time-window of length TTW from a depot located at a distance of ρx and ρy from the center of the service region. This depot – an e-commerce fulfillment center is serviced from a larger regional fulfillment center located at a distance of ρx', ρy' from the center of the service region. In addition, let there be NF randomly and uniformly distributed facilities within the service region, of which NMH operate as micro-hubs (consolidation facilities) and NCP are collection point pick-up facilities, serving a market share of pMH and pCP respectively. The vehicles departing from depot serve the N(1-pMH-pCP) customers directly and service the facilities as well, and the vehicles departing from MHs serve the market, while customers drive to the CPs to pick-up their packages.

This work employs Continuous Approximation (CA) techniques to model last-mile parcel delivery operations to better understand the impacts of different city logisitcs measures and last-mile strategies.