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.

翻译：取件点被广泛认为是家庭配送的可持续替代方案，因为将订单集中在取件点可以缩短配送路线并提高首次投递成功率。然而，当客户驾车前往取件点取货时，这些优势可能会被抵消。本研究提出一种差异化取件点推荐策略，旨在同时减少配送卡车路线和客户出行产生的排放。在该策略下，系统会为每位到达的客户推荐单一取件点，而非允许其在所有取件点中自由选择，同时保留家庭配送选项。我们在动态随机环境中研究该问题，其中为每位客户推荐的取件点取决于已实现客户位置与配送选择的历史数据。为设计有效的差异化取件点推荐策略，我们采用基于强化学习的方法，该方法综合考虑客户与取件点之间的空间关系及其对未来路线整合的影响。计算实验表明，差异化取件点推荐能显著降低总碳排放量。相较于纯家庭配送模式，所提策略最高可减少9%的总排放量；与包括无限制取件点选择和最近取件点分配在内的替代策略相比，平均减排效果达2%。在取件点密集且站点间距较短的稠密城区环境中，差异化推荐策略尤为有效。此外，当客户对取件点配送的接受意愿低于家庭配送时，显式考虑客户到达与选择的动态特性显得尤为重要。