MS, Kent State University, 2014, College of Education, Health and Human Services / School of Foundations, Leadership and Administration

The objective of this research is to investigate the core financial analytical competencies that hospitality managers must possess. Similar measurement was employed in both the industry practitioners' and the educators' survey, as the aim was to explore the importance and use of revenue and cost management competencies among manager participants and compare the findings with those obtained from the educators' survey. The results of this study showed that cost control, followed by revenue management, budgeting, forecasting, pricing, and asset management, are the essential financial competencies of hotel managers. The perception gap was found between hospitality managers and educators on the importance of these financial competencies. The findings of this study could provide suggestions for hospitality education and industry training programs.

Committee: Ning-Kuang Chuang (Committee Chair); Amy Gregory (Committee Member); Aviad Israeli (Committee Member) Subjects: Management

Doctor of Philosophy (PhD), Ohio University, 2015, Industrial and Systems Engineering (Engineering and Technology)

The long-term permanency and profitability of a firm requires decisions to be made wisely and on time. For this purpose, it is essential to consider all aspects of a decision in terms of its impact on revenue, planning, scheduling, and inventory in an integrated framework. In this research paper, an interactive intelligent decision support system for making an integrated decision in the presence of demand uncertainty is proposed. The system operates in a multi-product, multi-period setting, and its objective is to maximize the profit of the firm over time. To achieve its objective, the system first obtains the optimal price and capacity plan for the coming periods. The output of this first step becomes the input for the second step, in which the problem of scheduling is solved. At the end, based on the scheduling step, the optimum inventory policy is determined. To cope with demand uncertainty in the pricing and planning phase, a robust optimization model is proposed in which the demand is considered to belong to an interval and there is no knowledge (such as statistical distribution) associated with the demand. The robust problem is solved using a metaheuristic. During the scheduling step, a general setting for the problem is considered, in which each product is treated like a project with a flow network. To address the problem of scheduling, a simulation optimization method is applied in which the optimization step determines the dispatch rule of the jobs and the simulation step schedules the dispatched jobs on the production line. During the inventory step, the system obtains the best schedule for ordering and storing the raw material in order to minimize the inventory cost. For this purpose, a mixed integer mathematical model is proposed and a metaheuristic is applied to obtain the best solution. All modules of the proposed decision support system are supported with a database that stores the data obtained from the shop floor and the market. This (open full item for complete abstract)

Committee: Gary Weckman Ph.D. (Advisor); Namkyu Park Ph.D. (Committee Member); William Young Ph.D. (Committee Member); Andy Snow Ph.D. (Committee Member); Hajrudin Pasic Ph.D. (Committee Member) Subjects: Industrial Engineering; Information Systems; Systems Science

Doctor of Philosophy, Case Western Reserve University, 2005, Operations Research

Since the service is an economic good, imposing an admission price often provides an efficient means for matching supply and demand. On the other hand an important dimension of providing a service is the time the customer has to wait, especially in situations where the time of delivery is the single factor apart from price that determines whether the customer will do business with the firm. These issues arise in a variety of situations in practice, ranging from manufacturing, to pure service systems, telecommunications, call centers and high bandwidth lines for data transfer. The problem becomes more involved when the potential customers differ in their sensitivity to waiting. In the first part of this work we classify customers into two classes, based on their sensitivity to delay and study the effects of price discrimination on revenue. Next, we consider a system without the ability to price discriminate. We suggest splitting the available capacity into two servers and advertising different entrance fees. Finally, we propose a mechanism that employs options for service as means to increase system flexibility, customer satisfaction and revenue generation. It is typical in this line of research to assume that, upon arrival to the system the customer is given the opportunity to select the mode of service and the corresponding price without any restrictions, and, in many cases, is informed about the level of congestion in the system before he makes this selection. Although this is beneficial to the customers, it may make it harder for the service provider to plan for the necessary service capacity. There is thus a tradeoff between customer flexibility and service provider capacity planning that is often met in supply chains of physical products. In the last chapter of this thesis we propose a mechanism that addresses this tradeoff by employing options for service. The options are offered at a fixed price per unit. A customer purchases a number of them and for a specif (open full item for complete abstract)

Committee: Apostolos Burnetas (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2013, Operations

This dissertation consists of three essays that analyze stochastic dynamic optimization models and game models in operations management. Markov decision processes (MDPs) and sequential games are good models of many real sequential decision processes. However, in diverse applications in operations research and economics, the state of the MDP is a vector and the curse of dimensionality obstructs analysis and computations. Several veins of research seek to exorcise this curse. One vein, the domain of the first essay, identifies MDPs with myopic optima, namely sequential decision processes that can be solved via a temporal sequence of static problems. The first essay identifies new classes of MDPs with myopic optima and sequential games with myopic equilibrium points. Seasonal fashion goods and seats on specific airline fights exemplify a job lot whose sale as time passes should be managed closely as a deadline approaches. The second essay analyzes a dynamic revenue management model in which firms set prices and hold back inventory for sale later in the season. We concentrate on a model with demand functions that are stochastic, nonstationary, and iso-elastic. If there is only a single firm, the resulting Markov decision process has a myopic optimum that can be specified nearly explicitly and is easily computed. If there are multiple firms, there is a myopic Markov-perfect equilibrium point that can be computed easily and facilitates the analysis of comparative dynamics. A pricing schedule is nonlinear if the resulting revenue is not a linear function of quantity. The third essay presents a dynamic nonlinear pricing model with stochastic demand. We start with a two-segment model for illustrative purposes and derive properties of the model and its optimum which include the existence of a myopic optimum that can be specified nearly explicitly and is easily computed. Furthermore, the optimal policy is a linear decision rule. The essay ends by extending the two- segment (open full item for complete abstract)

Committee: Matthew Sobel (Committee Chair); Peter Ritchken (Committee Member); Kamlesh Mathur (Committee Member); Ankur Goel (Committee Member) Subjects: Operations Research