Demand-Driven Analysis for Meal Delivery: Delineation, Adaptation, and Optimization
Open Access DepositedThe COVID-19 pandemic has caused unprecedented damage to restaurant businesses, especially indoor dining services, due to the widespread fear of coronavirus exposure. In contrast, the online food ordering and delivery services, led by DoorDash, Grubhub, and Uber Eats, filled in the vacancy and achieved explosive growth. As a result, the restaurant industry is experiencing dramatic transformations under the crossfire of these two driving forces. In this dissertation, we will first leverage foot traffic data to gain insights into the landscape of demand in the restaurant industry, examining the structural changes potentially catalyzed by the availability and growing popularity of meal delivery services. To support the geographic expansion of meal delivery, the dissertation investigates innovations in delivery paradigms through the integration of emerging vehicular technologies and supporting infrastructure, tailored to varied service demand contexts. Urban environments are characterized by high demand density, with meal orders generated intensively from business districts and residential complexes and fulfilled by a wide range of restaurants distributed across the city. In such settings, meal delivery platforms operate under a many-to-many distribution structure, where efficiency critically depends on the ability to batch orders by overcoming potential misalignments in pickup and delivery locations and times. To address these challenges, we propose a transshipment strategy in which meal packages are transferred through a centrally placed microhub rather than being picked up and delivered entirely by a single deliverer. This approach decouples the pickup and drop-off stages of delivery, unlocking improved batching opportunities and significantly enhancing operational efficiency, particularly during peak hours when delivery capacity is constrained. In contrast, suburban and rural areas in the U.S. present fundamentally different spatial patterns of both supply and demand for meal delivery services. Restaurants in these regions are often clustered in local plazas or town centers serving broader, more dispersed residential areas. These conditions align more closely with the one-to-many distribution models commonly studied in the transportation literature. For such low-density and temporally sparse demand settings, lightweight and low-capacity delivery modes can offer operational advantages. Rural areas, in particular, exemplify this demand sparsity and uncertainty, rendering high-capacity meal delivery models economically inefficient. In response, we explore the operations for drone delivery as a competitive alternative, offering lower per-mile costs and greater flexibility. To account for demand uncertainty, we develop a two-stage stochastic model implemented within a rolling-horizon framework to facilitate dynamic order fulfillment in rural contexts. Then, in the transitional territories between urban and rural areas, suburbs exhibit moderate yet sprawling demand. Accordingly, we prescribe a hybrid delivery model that combines the strengths of both heavy-duty and lightweight delivery means. In our envisioned operations, trucks operate along major arterials, while multiple drones sidekick alongside the trucks' trajectory, performing final package deliveries and minimizing truck detours. The research in this dissertation delves into these new operations and system designs to assess their potential benefits to the logistics and delivery industry in dynamic and contrasting environments. Overall, my dissertation contributes by proposing strategic enhancements to meal delivery operations and designs across diverse geographic contexts, aiming to bolster system efficiency and societal welfare. This work provides a comprehensive understanding of the evolution of meal delivery services, from its market foundation to the current landscape and future game-changers, while developing effective management tools to navigate these changes. Although this dissertation scopes out meal delivery services, the models, analyses, and solutions can be readily adapted to address related issues in other urban delivery services.
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