Some owners of small-farm wineries have moved to direct and alternative revenue management strategies to generate revenue and create brand awareness because of increased competition and regulatory changes. Research has revealed that owners of small-farm wineries remain financially reliant on direct-to-consumer sales through tasting rooms that represent an estimated 70% of their total revenue generated. This qualitative multiple case study was an exploration of how revenue management decisions of small-farm winery owners may contribute to long-term survival in a regulated industry. Dynamic capabilities concept was the conceptual framework for this study. The study population consisted of 3 small-farm winery owners in Connecticut who have operated a winery with Connecticut Grown designation for at least 10 years. Data were collected through semistructured interviews, organizational documents, observation notes, and review of each winery's website. Three themes emerged from data analysis: focus on brand and customer base, constraints consideration, and competitors' impact. The findings and recommendations from this study may further small-farm winery owners' understanding of revenue management strategies they can use to overcome constraint challenges and mitigate competitors' impact. As small-farm winery owners improve profitability and sustain long-term survival, subsequent positive social change, such as small business development and increased employment opportunities, may lead to economic prosperity for the local community and financial stability of community residents.

A fundamental challenge in operations management is to dynamically match spatially diversified supply sources with random demand units. This dissertation tackles this challenge in two major areas: in supply chain management, a company procures from multiple, geographically differentiated suppliers to service stochastic demands based on dynamically evolving inventory conditions; in revenue management of ride-hailing systems, a platform uses operational and pricing levers to match strategic drivers with random, location and time-varying ride requests over geographically dispersed networks. The first part of this dissertation is devoted to finding the optimal procurement and inventory management strategies for a company facing two potential suppliers differentiated by their lead times, costs and capacities. We synthesize and generalize the existing literature by addressing a general model with the simultaneous presence of (a) orders subject to capacity limits, (b) fixed costs associated with inventory adjustments, and (c) possible salvage opportunities that enable bilateral adjustments of the inventory, both for finite and infinite horizon periodic review models. By identifying a novel, generalized convexity property, termed (C1K1, C2K2)-convexity, we are able to characterize the optimal single-source procurement strategy under the simultaneous treatment of all three complications above, which has remained an open challenge in stochastic inventory theory literature. To our knowledge, we recover almost all existing structural results as special cases of a unified analysis. We then generalize our results to dual-source settings and derive optimal policies under specific lead time restrictions. Based on these exact optimality results, we develop various heuristics and bounds to address settings with fully general lead times. The second part of this dissertation focuses on a ride-hailing platform's optimal control facing two major challenges: (a) significant demand imbalances across the network, and (b) stochastic demand shocks at hotspot locations. Towards the first major challenge, which is evidenced by our analysis of New York City taxi trip data, the dissertation shows how the platform's operational controls--including demand-side admission control and supply-side empty car repositioning--can improve system performance significantly. Counterintuitively, it is shown that the platform can improve the overall value through strategic rejection of demand in locations with ample supply capacity (driver queue). Responding to the second challenge, a demand shock of uncertain duration, we show how the platform can resort to surge pricing and dynamic spatial matching jointly, to enhance profits in an incentive compatible way for the drivers. Our results provide distinctive insights on the interplay among the relevant timescales of different phenomena, including rider patience, demand shock duration and drivers' traffic delay to the hotspot, and their impact on optimal platform operations.

This thesis explores the institutional choices available to Timor-Leste to manage their natural resource wealth wisely and avoid the resource curse. Timor-Leste is a poor country and its challenge is to use its large per capita resource wealth to alleviate poverty and enable sustainable development. This research examines the Petroleum Fund Law, and other mechanisms to manage petroleum revenue that the Government of Timor-Leste has established. These mechanisms appear to be resilient, but remain untested. Based on field interviews in Timor-Leste, the study offers insights into the opinions of East Timorese and foreign advisers about how Timor-Leste´s petroleum revenue should be managed, and how a poor country can raise the living standards of its people.¶A framework that identifies human and social capital as essential to the quality of institutions is developed in this research, which proposes that the pre-condition of institutions affects the management of natural resource revenue. As a result of history (not its natural resource wealth) Timor-Leste´s productive institutions are weak and destructive institutions, such as corruption, are strong. The preferences of the research participants, identified using semi-structured interviews and multi-criteria decision analysis, revealed that what petroleum revenue is spent on is the most important petroleum revenue management decision. Further, health and education were regarded the highest spending priorities. Petroleum revenue management decisions that may affect Timor-Leste´s economic, social and political independence were also important to participants.¶Timor-Leste´s sustainable development depends on continued assistance in the form of foreign advisers to address its lack of human capital. A commitment to transparency should counteract the lack of trust between government and civil society. Timor-Leste will also need to invest more in people, and recognise that the wise management of its petroleum revenue depends as much on good governance as the mechanisms designed to manage it. The people of Timor-Leste´s fierce determination to overcome the challenges they face, against all odds, may help Timor-Leste to avoid the resource curse.¶

This paper analyzes macroeconomic factors and their effect on 2-year government bonds of 11 countries in the European Monetary Union. I specifically looked at how a simultaneous budget and trade surplus effect a country's bond yield spread relative to Germany's bond yield. My model showed that double surplus countries have a larger yield spread than countries that do not have a double surplus.

This thesis aims to improve sales forecasting in the context of passenger airlines. We study two important issues that could potentially improve forecasting accuracy: day-to-day price change rather than price itself, and linking flights that are likely to be considered as pairs for a round trip by passengers; we refer to the latter as the Round-Trip Model (RTM). We find that price change is a significant variable regardless of days remaining to flight in the last three weeks to flight departure, which opens the possibility of planning for revenue maximizing price change patterns. We also find that the RTM can improve the precision of the forecasting models, and provide an improved pricing strategy for planners. In the study of the effect of price change on sales, analysis of variance is applied; finite regression mixture models were tested to identify linked traffic in the two directions and the linked flights on a route in reverse directions; adaptive neuro-fuzzy inference system (ANFIS) is applied to develop comparative models for studying sales effect between price and price change, and one-way versus round-trip models. The price change model demonstrated more robust results with comparable estimation errors, and the concept model for the round-trip with only one linked flight reduced estimation error by 5%. This empirical study is performed on a database with 22,900 flights which was obtained from a major North American passenger airline.

Practical inventory settings often include multiple generations of the same product on hand. New products often arrive before old stock is exhausted, but most inventory models do not account for this. Such a setting gives rise to the possibility of inter-generational substitution between products. We study a retailer that stocks two product generations and we show that from a cost perspective the retailer is better off stocking only one generation. We proceed with a profit scheme and develop a price-setting profit maximization model, proving that in one and two generation profit models there exists a unique solution. We use the profit model to show that there are cases where it is more profitable to stock two generations. We discuss utility and preference extensions to the profit model and present the general n-product case.

Robust Airline Fleet Assignment Barry C. Smith 140 Pages Directed by Dr. Ellis L. Johnson Fleet assignment models are used by many airlines to assign aircraft to flights in a schedule to maximize profit. Major airlines report that the use of fleet assignment models increases annual profits by more than $100 million. The results of fleet assignment models affect subsequent planning, marketing and operational processes within the airline. Anticipating these processes and developing solutions favorable to them can further increase the benefits of fleet assignment models. We propose to produce fleet assignment solutions that increase planning flexibility and reduce cost by imposing station purity, limiting the number of fleet types allowed to serve each airport in the schedule. We demonstrate that imposing station purity on the fleet assignment model can limit aircraft dispersion in the network and make solutions more robust relative to crew planning, maintenance planning and operations. Because station purity can significantly degrade computational efficiency, we develop a solution approach, Station Decomposition, which takes advantage of airline network structure. Station Decomposition uses a column generation approach to solving the fleet assignment problem; we further improve the performance of Station Decomposition by developing a primal-dual method that increases the solution quality and model efficiency. Station Decomposition solutions can be highly fractional; we develop a fix and price heuristic to efficiently find integer solutions to the fleet assignment problem. Airline profitability can be increased if fleet assignment models anticipate the effects of marketing processes such as revenue management. We develop an approach, ODFAM, which incorporates airline revenue management effects into the fleet assignment model. We develop an approach to incorporate station purity and ODFAM using a combination of column and cut generation. This approach can increase airline profit up to $27 million per year.

In this thesis, airline network revenue management problem is considered for the case with no cancellations and overbooking. In literature, there exist several approximate probabilistic and deterministic mathematical models developed in order to maximize expected revenue at the end of the reservation period. The aim of this study is to develop models considering also the risks involved in the proposed booking control policies. Two linear programming models are proposed which incorporate the variance of the revenue. The objective of the models is to effectively balance the tradeoff between the expectation and variance of the revenue. The performances of the proposed models are compared to the previous models through a numerical study. The seat allocations resulting from the mathematical models are used in a simulation model working with several booking control policies. The probability distributions of the revenues are investigated and the revenues are compared in terms of expectation, standard deviation, coefficient of variation and probability of poor performance. It is observed that the use of the proposed models decreases the variability of the revenue and thereby the risk of probability of poor performance. Also, the expected revenues obtained by implementing the solutions of the proposed models with nested booking control policies turn out to be higher than other probabilistic models as long as the degree of variance incorporation is within some interval. When compared with the deterministic models, the proposed models provides for the decision makers with alternative, preferable policies in terms of the expectation and the variability measures.

In this study, the seat inventory control problem is considered for airline networks from the perspective of a risk-averse decision maker. In the revenue management literature, it is generally assumed that the decision makers are risk-neutral. Therefore, the expected revenue is maximized without taking the variability or any other risk factor into account. On the other hand, risk-sensitive approach provides us with more information about the behavior of the revenue. The risk measure we consider in this study is the probability that revenue is less than a predetermined threshold level. In the risk-neutral cases, while the expected revenue is maximized, the probability of revenue being less than such a predetermined level might be high. We propose three mathematical models to incorporate the risk measure under consideration. The optimal allocations obtained by these models are numerically evaluated in simulation studies for example problems. Expected revenue, coefficient of variation, load factor and probability of the poor performance are the performance measures in the simulation studies. According to the results of these simulations, it shown that the proposed models can decrease the variability of the revenue considerably. In other words, the probability of revenue being less than the threshold level is decreased. Moreover, expected revenue can be increased in some scenarios by using the proposed models. The approach considered in this thesis is especially proposed for small scale airlines because risk of obtaining revenue less than the threshold level is more for this type of airlines as compared to large scale airlines.

The most important market segmentation in sports and entertainment industry is the competition between customers that buy bundle and single tickets. A common selling practice is starting the selling season with bundle ticket sales and switching to selling single tickets later on. The aim of this practice is to increase the number of customers that buy bundles, to create a fund before the season starts and to increase the load factor of the games with low demand. In this thesis, we investigate the effect of time dependent demand on dynamic switching times and the potential revenue gain over the case where the demand rate is assumed to be constant with time.