Alp Muzaffer Arslan

Problem definition: The rapid growth of on-demand delivery services (ODS) has transformed customers' online grocery shopping expectations. ODS provide increased convenience and speed through micro-fulfillment centers or partner-retail stores. Determining the most effective ODS fulfillment strategy across market settings, such as the presence or absence of micro-fulfillment centers and the appropriate design of the service region size, remains an open question. Additionally, the impact of uncertainty arising from inventory information inaccuracies, or the time to locate ordered products within the service region, leads to operational complexities and requires parsimonious models to assess performance at the strategic level. Methodology/results: We study this problem by developing a stylized queueing system that allows us to analytically assess an ODS's performance under a variety of configuration strategies with different service region sizes. Our model captures crucial ODS characteristics, including stochastic order arrivals, uncertain product availability, stochastic picking times, and tight service level requirements. We provide explicit expressions for calculating the service time and click-to-delivery time, which depend on factors such as assortment and stock-out uncertainties. Additionally, we extend this queueing model into an optimization framework where fulfillment configuration and service region size are treated as decision variables with the objective of maximizing operating profit. This formulation facilitates comparisons between various ODS configurations. Our findings suggest that ODS fulfillment configuration and service region sizing should be considered jointly at the investment or expansion phase. Managerial implications: For ODS operators considering the establishment of a micro-fulfillment center or partnership with grocery retailers, and their investors, careful consideration of these findings is crucial. The choice of fulfillment strategy and technology adoption should align with specific operational circumstances and objectives to ensure optimal performance and success in this evolving market.

This paper explores the logistics operations of instant grocery delivery services. Therefore, we introduce the instant delivery problem (IDP) to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. In the PSS, couriers visit affiliated brick-and-mortar stores in the delivery area to pick up and purchase ordered products and then deliver them to customers. In the IOD system, couriers collect products from a single distribution center, or so-called dark store, in which the platform manages the inventory. Even though a PSS strategy is asset light because of the utilization of existing retailers in the area, maintaining a good level of on-time instant deliveries with the PSS is more complex than with IOD. This is because the PSS requires deciding which store to purchase ordered goods from, and picking and shopping at stores needs to be considered in the real-time decision process. We propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies both in real life–inspired settings and in case studies on selected urban areas show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders. We observe that the performance of the PSS is robust when varying the delivery service time frame. The case studies also suggest that the PSS becomes even more competitive in areas where the retail store density is high. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by National Natural Science Foundation of China [Grant 72150410444] to J.P. van der Gaast. Supplemental Material: The e-companion is available at https://doi.org/10.1287/serv.2022.0310 .

The trend toward shorter delivery lead times reduces operational efficiency and increases transportation costs for Internet retailers. However, mobile technology creates new opportunities to organize the last mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place. We consider a service platform that automatically creates matches between parcel delivery tasks and ad hoc drivers. The platform also operates a fleet of dedicated vehicles to serve the tasks that cannot be served by the ad hoc drivers. The matching of tasks, drivers, and dedicated vehicles in real time gives rise to a new variant of the dynamic pickup and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the matching problem each time new information becomes available. To investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the behavior of the ad hoc drivers. The results suggest that the use of ad hoc drivers has the potential to make the last mile more cost-efficient and can provide system-wide vehicle-mile savings up to 37% compared to a traditional delivery system with dedicated vehicles. The online appendix is available at https://doi.org/10.1287/trsc.2017.0803 .