Differentiated Pickup Point Offering for Emission Reduction in Last-Mile Delivery

TL;DR

This paper introduces a reinforcement learning-based differentiated pickup point policy that reduces last-mile delivery emissions by optimizing customer pickup options, especially in dense urban areas, balancing delivery efficiency and customer convenience.

Contribution

It proposes a novel DPO policy that dynamically recommends pickup points to minimize emissions, incorporating customer behavior and spatial factors in a stochastic setting.

Findings

Up to 9% reduction in total emissions compared to home-only delivery.

Average 2% emission reduction over alternative pickup policies.

Effectiveness is higher in dense urban environments with many pickup points.

Abstract

Pickup points are widely recognized as a sustainable alternative to home delivery, as consolidating orders at pickup locations can shorten delivery routes and improve first-attempt success rates. However, these benefits may be negated when customers drive to pick up their orders. This study proposes a Differentiated Pickup Point Offering (DPO) policy that aims to jointly reduce emissions from delivery truck routes and customer travel. Under DPO, each arriving customer is offered a single recommended pickup point, rather than an unrestricted choice among all locations, while retaining the option of home delivery. We study this problem in a dynamic and stochastic setting, where the pickup point offered to each customer depends on previously realized customer locations and delivery choices. To design effective DPO policies, we adopt a reinforcement learning-based approach that accounts for spatial relationships between customers and pickup points and their implications for future route consolidation. Computational experiments show that differentiated pickup point offerings can substantially reduce total carbon emissions. The proposed policies reduce total emissions by up to 9% relative to home-only delivery and by 2% on average compared with alternative policies, including unrestricted pickup point choice and nearest pickup point assignment. Differentiated offerings are particularly effective in dense urban settings with many pickup points and short inter-location distances. Moreover, explicitly accounting for the dynamic nature of customer arrivals and choices is especially important when customers are less inclined to choose pickup point delivery over home delivery.