Essays on Service Operations with Strategic Customers and Innovative Business Models
This dissertation studies operations management problems in service operations and supply chains, analyzing the impact of strategic customers and disruptive technologies. We investigate three problems, and insights from the study of each one illuminate the effect of information and/or strategic customer behavior on decision makers in operations management systems.
The first problem involves the strategic routing behavior of customers in a service network with multiple stations, when customers can choose the order of stations that they visit. We propose a two-station game with all customers present at the start of service and deterministic service times, and we find that strategic customers "herd," i.e., in equilibrium all customers choose the same route. For unobservable systems, we prove that the game is supermodular, and we then identify a broad class of learning rules---which includes both fictitious play and Cournot best-response---that converges to herding in finite time. By combining different theoretical and numerical analyses, we find that the herding behavior is prevalent in many other congested open-routing service networks, including those with arrivals over time, those with stochastic service times, and those with more than two stations. We also find that the system under herding performs very close to the first-best outcome in terms of cumulative system time.
The second problem relates to a disruptive, on-demand delivery platform who provides delivery from an independent sit-down restaurant. Food delivery platforms maintain a symbiotic relationship with the existing providers in their industry; rather than "stealing" demand from an established player, these platforms work with restaurants to connect customers with the restaurant's product by providing an additional purchase channel. We model the restaurant as a queueing system with customer waiting costs. First, we solve the revenue maximization problem faced by a monopolist who controls both the dine-in and delivery prices and receives all revenues from the system. These results are related to the priority queueing and pricing literature and are of independent interest. We also demonstrate that a coordination problem exists between the restaurant and platform. We then investigate means of coordinating this supply chain via different contracts between the restaurant and the platform. We find that a two-way revenue-sharing contract coordinates the supply chain.
Finally, our third problem is spawned from an industry collaboration aimed at improving the inventory planning decisions of a company that sells high-tech goods with short life cycles. We develop a novel heuristic based on a power approximation in the extant literature. The power approximation computes near-optimal (s,S) policies for infinite-horizon inventory problems. We propose a new form of this approximation, devised using real demand data from our industry partner, and a heuristic based on the approximation that updates the inventory policy as new demand forecasts are generated. We evaluate the performance of our heuristic---also on real demand data, though for a different time period and for different items than were used to fit our model---and find that it performs quite well.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Duke Dissertations