UBC Theses and Dissertations
Dynamic decision making in service operations management Imanpoor Yourdshahy, Mona
This thesis comprises three studies. Within each study, we analyze a stochastic multi-period decision making problem in the area of service management. Throughout the dissertation, we incorporate dynamic programming techniques to develop the mathematical models and analyze the solution approaches for each of the discussed problems. In the first two studies, we consider a queueing system in which customers require some conditions to be met prior to receiving service. We investigate whether an individual arriving to this system should join the queue at that time, or wait to join at some future time. Chapters 2 and 3 discuss two variations of this problem. We formulate the problem as a Markov decision process and show how the structure of the optimal policy depends on various parameters of the model. Furthermore, in Chapter 3, we use the construct of “level-k” thinking from the behavioral and experimental economics literature to model the bounded rationality of customers, and also to characterize their equilibrium strategy. We present the structural results of the customers’ joining policies and show the threshold structure of customers’ decisions with different levels of rationality. We also analyze the socially optimal solution and compare it to the equilibrium policy. The optimal policies we derive allow for a better management of customers’ waiting time and also give information on their joining behavior to service providers. In the third study, we analyze another dynamic decision making problem in a different context than the previous two studies. Through tracking drivers’ behavior, Usage Based Insurance (UBI) allows insurance companies to connect insurers’ premiums more closely to their actual driving performance. Chapter 4 provides a theoretical model to capture the effects of UBI on the auto insurance market. We formulate the underlying problem as a dynamic principal-agent model with hidden information and hidden action. Developing a dynamic programming algorithm, we characterize the full history-dependent optimal contract. Our model results shed light on how to design the contract to manage a UBI program, the extent to which a UBI policy can outperform a traditional policy, and how the potential gains depend on the demographics of the target market.
Item Citations and Data
Attribution-NonCommercial-NoDerivatives 4.0 International