Econ Papers Daily / 期刊论文
流动零售：时间敏感的旅行商问题
Retail on Wheels: A Time-Sensitive Traveling Salesman Problem
摘要
Rapid advancement in self-driving technology has brought mobile retail into the spotlight. Motivated by its potential, we present a model, theory, and insights into the operations of mobile retail, where a fleet of wheeled stores traverses a service region, selectively seeks locations in which to perch, and fulfills local customer demand. The goal of retailers is to strike a balance between perching and migrating to maximize total revenue. To dispatch such stores, retailers need to consider the following time-sensitive factors: (i) how busy the service is, (ii) how soon customers will arrive, and (iii) how quickly freshness decays. We propose and analyze a model that incorporates these factors into the classical traveling salesman problem (TSP), which we call the “time-sensitive TSP.” Using the continuous approximation approach, we derive the optimal revenue as well as the upper and lower bounds in the asymptotic regime as candidate locations increase to infinity. The tightness of the bounds is validated. Our analysis demonstrates that store mobility creates value by bringing proximity to customers. We further uncover the effects of these time-sensitive features on decision making. Specifically, the scaling relationship between revenue and fleet size is affected by customer tardiness and busyness. Busyness determines stores’ ability to expand their service, whereas tardiness affects how effectively stores can utilize the initial high-freshness period. These findings deepen our understanding of mobile retail operations. This paper was accepted by Karan Girotra, operations management. Funding: J. Cao acknowledges the support from McCombs Research Excellence Grant. W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72242106, 72188101, and 72521001]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04259 .
中文摘要
自动驾驶技术的快速发展使移动零售成为关注焦点。受其潜力驱动，本文提出一个关于移动零售运营的模型、理论与洞见：在该模式中，一支由轮式商店组成的车队在服务区域内巡行，选择性地寻找停驻地点，并满足本地客户需求。零售商的目标是在停驻与迁移之间取得平衡，以最大化总收益。为调度此类商店，零售商需考虑以下时间敏感因素：（i）服务繁忙程度，（ii）客户到达的快慢，（iii）新鲜度衰减的速度。我们提出并分析了一个将这些因素纳入经典旅行商问题（TSP）的模型，称之为“时间敏感TSP”。采用连续近似方法，我们推导了当候选地点数量趋于无穷时的渐近情形下的最优收益及上下界，并验证了界的紧致性。我们的分析表明，商店移动性通过贴近客户创造价值。我们进一步揭示了这些时间敏感特征对决策的影响。具体而言，收益与车队规模之间的 scaling 关系受客户迟到与繁忙程度影响。繁忙程度决定商店扩展服务的能力，而迟到程度影响商店有效利用初始高新鲜度时期的程度。这些发现加深了我们对移动零售运营的理解。本文由运营管理领域的Karan Girotra接收。资助：J. Cao感谢McCombs Research Excellence Grant的支持。W. Qi感谢国家自然科学基金[ grants 72242106、72188101和72521001]的支持。补充材料：在线附录和数据文件可在https://doi.org/10.1287/mnsc.2023.04259获取。