A framework for determining energy use in rural food delivery services: capturing system interdependencies through an agent-based discrete-event approach

Abstract

Abstract Food e-commerce has seen significant growth over the past decade that accelerated after the onset of the COVID-19 pandemic. Last-mile transportation and logistics are widely considered the most expensive and least efficient portion of the supply chain and have multiple important energy trade-offs such as cargo capacity and consumer density. Last-mile transportation energy use in rural areas is underrepresented in the literature. This study proposes a hybrid agent-based and discrete event model framework for evaluating the last-mile transportation energy use of van- and car-based food delivery services in a rural community, based on meal-kit and grocery delivery operations, respectively. This framework quantifies last-mile energy use in rural areas, and is demonstrated here using a neighborhood outside of Austin, TX as an analytical testbed. The study focuses on the effects of consumer density, cargo limitations, and vehicle speed. For the conditions examined with this framework, diesel delivery vans use more total energy than passenger cars for the same trip, though a van delivering four orders uses less energy per-order than a car delivering one order. However, there are trade-offs between vehicle type and mileage, cargo capacity, route density, and speed that are particularly important for delivery services operating in rural areas. This framework can be used by service providers to assess route-specific trade-offs for each vehicle and gauge which is preferable for given operating conditions or to evaluate the energy, and thus also cost, impact of expanding their services to rural areas.