Retail on Wheels: A Time-Sensitive Traveling Salesman Problem

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.