混合最后一英里配送中的品类控制释放动态定价的价值
Assortment Control Unlocks the Value of Dynamic Pricing in Mixed Last-Mile Delivery
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中文总结 AI 辅助
针对混合最后一英里配送，提出基于近似动态规划的状态价值锚定定价方法，结合品类控制，在西雅图实例上提升利润7%，证明定价与品类控制互补。
中文摘要 AI 辅助
电子商务的增长加剧了对能够联合管理客户选择与运营效率的最后一英里配送系统的需求。我们研究了混合配送选项的动态提供与定价问题，在该问题中，物流服务提供商为顺序到达的客户动态选择并定价上门配送时段和店外自取选项。每个决策都会影响即时收入、客户接受度以及预订周期结束时实现的与路线相关的履约成本。我们将DOPMDO建模为有限时域马尔可夫决策过程，并提出基于近似动态规划的状态价值锚定定价方法。该方法在混合配送系统的聚合状态表示上学习续值近似，并使用接受与拒绝的价值差异来估计选项层面的机会成本。这些机会成本被嵌入到一个围绕校准的精细静态基准的锚定信赖域定价问题中。在真实世界西雅图实例上的计算实验表明，与当前实践相比，SVAP--ADP将平均回合利润提高了7.0%（95%置信区间：6.7--7.2%），主要通过降低终端履约成本同时保持稳定的家庭--储物柜--选择退出组合来实现。品类控制实验表明，当菜单展示具有运营价值的储物柜替代方案时，动态定价最为有效，更丰富的候选池比受限的最近储物柜菜单带来更大的收益。这些结果表明，预期性定价与品类控制是互补的：定价将客户引导至成本更低的选项，但菜单决定了高价值整合机会是否首先可用。
英文摘要
The growth of e-commerce has intensified the need for last-mile delivery systems that can jointly manage customer choice and operational efficiency. We study the Dynamic Offering and Pricing of Mixed Delivery Options problem, in which a logistics service provider dynamically selects and prices attended home-delivery time slots and out-of-home pickup options for sequentially arriving customers. Each decision affects immediate revenue, customer acceptance, and the route-dependent fulfillment cost realized at the end of the booking horizon. We formulate DOPMDO as a finite-horizon Markov decision process and propose State-Value Anchored Pricing via Approximate Dynamic Programming. The method learns a continuation-value approximation on an aggregate state representation of the mixed-delivery system and uses accepted-versus-rejected value differences to estimate option-level opportunity costs. These opportunity costs are embedded in an anchored trust-region pricing problem around a calibrated fine-static benchmark. Computational experiments on the real-world Seattle instance show that SVAP--ADP increases mean episode profit by 7.0\% relative to current practice (95\% CI: 6.7--7.2\%), primarily by reducing terminal fulfillment cost while maintaining a stable home--locker--opt-out mix. Assortment-control experiments show that dynamic pricing is most effective when the menu exposes operationally valuable locker alternatives, with richer candidate pools delivering substantially larger gains than restricted nearest-locker menus. These results indicate that anticipatory pricing and assortment control are complementary: pricing steers customers toward lower-cost options, but the menu determines whether high-value consolidation opportunities are available in the first place.
发表机构
- Chalmers University of Technology（查尔姆斯理工大学）
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