UC Irvine

This dissertation explores changes in the channels used for consuming prepared food (restaurant meals) and proposes optimization approaches for better managing a fleet of delivery vehicles. In the context of the COVID-19 pandemic, Chapter 1 examines how the consumption of prepared meals has evolved in California, with meal delivery gaining in popularity, dine-in experiences shrinking, and takeout witnessing marginal growth. I estimated heterogeneous ordered logit models to explain the frequency of consumption of restaurant meals before, during, and possibly after the pandemic for dine-in, takeout, and online orders with delivery using a broad range of explanatory variables, including components of the Social Vulnerability Index (SVI). My results show disparities in dine-in, takeout, and delivery frequencies, which have implications for equitable access to prepared meals.Chapter 2 extends my investigation to meal delivery in California and contributes to the traditional Food-Away-From-Home (FAFH) literature. I estimate spatial Durbin models to explain the demand for monthly meal delivery at the census tract level in three major MSAs (Metropolitan statistical areas) in California before and during the pandemic. Unique dynamics in meal delivery behavior emerge across regions and time, with accessibility proving pivotal in driving demand. In particular, I find that meal deliveries benefitted marginalized communities, which underscores the role of meal deliveries in enhancing food access. This chapter presents a holistic perspective, which encompasses business strategies and discusses policy implications. Chapter 3 explores a fleet management framework for meal delivery platforms based on graph theory optimization algorithms. I identified critical parameters for meal delivery operations and measured platform performance metrics such as Vehicle Hours Traveled (VHT), Vehicle Miles Traveled (VMT), and fleet size by adjusting the parameters. The comparative analysis of the Hopcroft-Karp and Karp algorithms reveals trade-offs between cost minimization and computational complex based on the algorithmic objects. My evaluation of Proposition 22’s impact on platform costs underscores the importance of modeling legal constraints. This chapter provides practical insights for platform operators to optimize service efficiency. It also provides directions for future research for more realistic simulations, including a dynamic approach, vehicle repositioning strategy, and consideration of different modes. Overall, this dissertation helps understand dynamic shifts in prepared meal consumption and delivery, and shows the importance of modeling legal constraints when optimizing the size of a delivery fleet. Findings could guide equitable policy interventions by highlighting the influence of demographic, regional, and economic factors on the frequency of restaurant meal consumption. My research bridges academia and practices through its interdisciplinary approach, which helps promote informed decision-making for platform managers, restaurant owners, and equity-conscious urban planners.