Essays on online hyperlocal food service marketplaces

Abstract

Owing to the e-commerce revolution, food service business is shifting from being the traditional brick-and-mortar dine-in restaurant to being an online food ordering business. Although the online food service delivery business is gaining mass acceptance all over the world, there appears to be limited academic literature in this field. To the best of our knowledge, the papers presented in this thesis are the probably the first ones to elucidate on the structure and strategies of an online hyperlocal food service marketplace. First, we map the entire food service industry and identify a spectrum of business models that exist currently. We provide an introduction to the platform based business models and elaborate on their stages of evolution. We end our discussion with the online hyperlocal food service marketplace model, and provide a brief overview of its operations decisions in the form of a planning hierarchy of strategic and operational decisions. At a strategic level, we examine the supply network design (SND) decisions of an online hyperlocal food service marketplace/aggregator that operates its own quick service restaurants (QSR) by modelling the decisions regarding the number of sites that the aggregator needs to open, the capacity needed at these sites, and capacity to be reserved at third party restaurants participating in the marketplace while incorporating demand fulfillment, maximum service radius within which restaurants operate, and the minimum business guarantee considerations. At an operational level, we deal with the vehicle routing problem at a food service marketplace. We present an MILP formulation which provides optimal solutions for small instances within the operational planning time window. For larger instances, as the MILP fails, we provide a Constraint Programming formulation to overcome the operationalisation issue. We finally present a simulation based system to compare between various routing rules, order assignment rules, and travel modes in order to capture real life operational issues such as vehicle breakdowns, vehicle congestion, route availability (route blockages), etc.