Browsing by Author "Shang, Kevin"

This dissertation studies capacity investments in energy sources, with a focus on renewable technologies, such as solar and wind energy. We develop analytical models to provide insights for policymakers and use real data from the state of Texas to corroborate our findings.

We first take a strategic perspective and focus on electricity pricing policies. Specifically, we investigate the capacity investments of a utility firm in renewable and conventional energy sources under flat and peak pricing policies. We consider generation patterns and intermittency of solar and wind energy in relation to the electricity demand throughout a day. We find that flat pricing leads to a higher investment level for solar energy and it can still lead to more investments in wind energy if considerable amount of wind energy is generated throughout the day.

In the second essay, we complement the first one by focusing on the problem of matching supply with demand in every operating period (e.g., every five minutes) from the perspective of a utility firm. We study the interaction between renewable and conventional sources with different levels of operational flexibility, i.e., the possibility

of quickly ramping energy output up or down. We show that operational flexibility determines these interactions: renewable and inflexible sources (e.g., nuclear energy) are substitutes, whereas renewable and flexible sources (e.g., natural gas) are complements.

In the final essay, rather than the capacity investments of the utility firms, we focus on the capacity investments of households in rooftop solar panels. We investigate whether or not these investments may cause a utility death spiral effect, which is a vicious circle of increased solar adoption and higher electricity prices. We observe that the current rate-of-return regulation may lead to a death spiral for utility firms. We show that one way to reverse the spiral effect is to allow the utility firms to maximize their profits by determining electricity prices.

This dissertation studies how a subscription service provider offers contracts to customers without full information on their preferences. The first essay studies a mechanism design problem for business interruption (BI) insurance. More specifically, we study how an insurer deals with adverse selection and moral hazard when offering BI insurance to a firm. The firm makes demand forecasts and can make a recovery effort if a disruption occurs; both are unobservable to the insurer. We first find that because of the joint effect of limited production capacity and self-impelled recovery effort, the firm with a lower demand forecast benefits more from the BI insurance than that with a higher demand forecast. Anticipating a higher premium, the low-demand firm has an incentive to pretend to have the higher demand forecast to obtain more profit. We then characterize the optimal insurance contracts to deal with information asymmetry and show how the firm's operational and informational characteristics affect the optimal insurance contracts. We also analyze the case where the firm can choose its initial capacity and find that from the firm's perspective, capacity and BI insurance could be either substitutes or complements.The second essay focuses on the learning-and-earning trade-off in subscription service offerings. We consider a service provider offering a subscription service to customers over a multi-period planning horizon. The customers decide whether to subscribe according to a utility model representing their preferences for the subscription service. The provider has a prior belief about the customer utility model. Adjusting the price and subscription period over time, the provider updates its belief based on the transaction data of new customers and the usage data of existing subscribers. The provider aims to minimize its regret, namely the expected profit loss relative to a clairvoyant with full information on the customer utility model. To analyze regret, we first study the clairvoyant's full-information problem. The resulting dynamic program, however, suffers from the curse of dimensionality. We develop a customer-centric approach to resolve this issue and obtain the optimal policy for the full-information problem. This approach strikes an optimal balance between immediate and future profits from an individual customer. When the provider does not have full information, we find that a simple and commonly used certainty-equivalence policy, which learns only passively, exhibits poor performance. We illustrate that this can be due to incomplete or slow learning, but it can also occur because of offering a suboptimal contract with a long subscription period in the beginning. We propose a two-phase learning policy that first focuses on information accumulation and then profit maximization. We show that our policy achieves asymptotically optimal performance with its regret growing logarithmically in the planning horizon. Our results indicate that the provider should be cautious about offering a long subscription period when it is uncertain about customer preferences.

Firms' inventory or production decisions are influenced by a variety of factors, including both the selling price of the end products and the purchasing cost of raw materials. In most cases, there is a strong connection between purchasing costs and selling prices. In my dissertation, I study the impact of prices on a firm's inventory

decisions, particularly in systems with delivery lead time and environmental concerns. The findings are reported in three studies. The first study analyzes the joint inventory and pricing problem with lead time, which is known to be difficult to solve due to its computational complexity. We develop a simple heuristic to resolve

the computational issue and reveal the impact of lead time on the joint decisions. In the second study, we extend the heuristic approach in the previous study to systems with both positive lead time and fixed ordering costs. The effectiveness of the heuristic in both studies are verified through both theoretical bounds and numerical experiments. In the third study, we examine the effect of the procurement cost and its volatility on a firm's profit. This allows us to study under what conditions a firm can profitably operate an eco-friendly supply chain. Our study also helps the firms to understand what type of products would better absorb the higher costs associated with an eco-friendly production system.

This dissertation studies the integration of material and financial flows in supply chains, with the goal of examining the impact of cash flows on the individual firm's decision making and the overall supply chain efficiency. We develop analytical models to provide effective policy recommendations and derive managerial insights.

We first consider a credit-constrained firm that orders inventory to satisfy stochastic demand in a finite horizon. The firm provides trade credit to the customer and receives it from the supplier. A default penalty is incurred on the unfulfilled payment to the supplier. We utilize an accounting concept of working capital to obtain optimal and near-optimal inventory policies. The model enables us to suggest an acceptable purchasing price offered in the supplier's trade credit contract, and to demonstrate how liquidity provision can mitigate the bullwhip effect. We then study a joint inventory and cash management problem for a multi-divisional supply chain. We consider different levels of cash concentration: cash pooling and transfer pricing. We develop the optimal joint inventory replenishment and cash retention policy for the cash pooling model, and construct cost lower bounds for the transfer pricing model. The comparison between these two models shows the value of cash pooling, although a big portion of this benefit may be recovered through optimal transfer pricing schemes. Finally, we build a supply chain model to investigate the material flow variability without cash constraint. Our analytical results provide conditions under which the material bullwhip effect exists. These results can be extended to explain the similar effect when financial flows are involved. In sum, this dissertation demonstrates the importance of working capital and financial integration in supply chain management.

This dissertation studies dynamic pricing in service systems where the system state is defined as the wait time. The first essay studies a single-server queue where customers arrive according to a Poisson process. The service provider announces the price rate and current system wait time to incoming customers, who decide whether to join the queue and determine their service duration. The objective is to maximize either the long-run average revenue or social welfare. The problem is formulated as a continuous-time control model, and we develop an innovative method to obtain the optimal control policy. The optimal dynamic pricing policy reveals the compensation effect, where the service provider lowers the price rate when the wait time exceeds a threshold, in addition to the usual congestion effect. A numerical study demonstrates the superiority of the revenue-maximizing pricing policy over static pricing policies, especially for low arrival rates and impatient customers. The extension to nonlinear pricing and heterogeneous customers yields similar policy insights, showcasing the value of considering customer characteristics in dynamic pricing models. The proposed model can be utilized to design dynamic pricing schemes for fast-charging stations.

The second essay addresses a mechanism design problem for a single-server queue with customers arriving according to a Poisson process and possessing private information about their wait time sensitivity. Following a direct mechanism, where the service provider announces the system wait time and offers a menu of options to each arriving customer. By choosing an option or opting out, customers aim to maximize their utility. The objective is to design a mechanism that maximizes the long-run average revenue. The optimal mechanism is wait-time dependent and admits customers with lower wait-time sensitivities. The model reveals strategic complementarity between admission decisions and service times which became the admission threshold, and offered service time decreases as the wait time increases. Comparisons with simpler heuristic mechanisms quantify the value of the optimal mechanism, showing significantly higher revenue generation, particularly for moderate service costs and arrival rates. Modifying service times becomes crucial when considering the different customer types and their interaction with wait time.

The third essay investigates a queueing system where the firm strategically determines the release time of each arriving request. We consider a first-come-first-serve single-server system, with customer requests arriving according to a Poisson process. The base model includes two types of customers: impatient and patient, characterized by their privately known service valuations and time sensitivities. The chapter explores the potential of strategically delaying the release of products to improve system performance. It reveals that such a delay occurs when the proportion of impatient customers is high and the system wait time is shorter than the threshold. Importantly, the optimal inflated release time does not vary with the system wait time, facilitating practical implementation. The extension to continuous-type customers confirms the tangible impact of strategic delay on revenue improvement, particularly when faced with uncertainty in the types of arriving requests.