This thesis addresses two issues which arise in the context of airline revenue management. In the first part of the thesis, we develop an incentive mechanism to prevent revenue leakage caused by customers buying down. In the second part of the thesis, we discuss the revenue sharing problem between alliance partners and develop a mechanism by which the combined revenue can be distributed fairly among them. Situations which give rise to impossibility and possibility results are established. The practice of revenue management, employs the principle of differential pricing of a product based on various product restrictions. These product restrictions segment the market in such a manner so as to maximize the revenue. Airline industry which pioneered the practice of revenue management generally prices low for those who book early and high for those who book late for essentially the same seat. The low-fare products are targeted towards the market segment comprising of those customers who have a low valuation (reservation price) for the product (who are typically leisure customers, also called as low-fare customers).The high-fare product, on the other hand is targeted at the market segment comprising of customers who have a high valuation (reservation price) for the product (business class customers, also called as high-fare customers). However, it may happen that customers with high valuation for the product may also buy the low-fare product if it is available. This behavior of high-fare customers buying a low-fare product due to its availability is called the customer buy-down behavior. Such a customer behavior causes revenue leakage to the airline industry. Revenue management literature that primarily focuses on pricing and seat inventory control does not account for the customer buy-down behavior. In Part I of the thesis we address this issue of customer buy-down behavior. We develop an incentive mechanism in the form of a new product bundle which would attract only the high-fare customer. High fare customers such as business class customers typically have repeated travel plans, while low fare customers such as leisure travelers typically do not travel repeatedly. The proposed incentive mechanism takes advantage of this characteristic of high fare customers that distinguishes them from the low fare customers. In general, high fare product permits cancellation and does not impose any travel restrictions, and a low fare product, on the other hand does not permit cancellation and has other travel restrictions associated with them. A high fare customer with potential future travel plan might associate uncertainties with respect to travel dates and his ability to procure a low fare ticket for future travel. This uncertainty is exploited in the proposed product bundle. The new product bundle permits the customer to cancel the ticket for the future journey and relaxes the restrictions associated with the requested day and the future travel day. Such incentives would attract only the high fare customer and the low-fare customer will not be enticed by this product bundle. This is because the low fare customer is a one-off traveler. Thus, the acceptance of the product bundle by the customer reveals that he is a high-fare customer and its denial reveals that he is truly a low-fare customer. We determine the optimal price to be charged for each of the days (requested day and the future travel day) and the refund value for the future travel day. We find that multiple optimal solutions exist, and its existence indicate a win-win situation for both the customer and the seller. The customer benefits through the incentives offered and the seller benefits in the form of additional revenue that is achieved in the process of preventing revenue leakage. In Part II of the thesis, we discuss the revenue sharing problem between alliance partners of a network. Airlines form alliances and coordinate through activities such as code sharing, scheduling of flight arrival and departure times, arrival and departure gates, frequent flyer programs, airport lounges and ground facilities among several others. Code sharing is a key feature among the coordinated activities of alliance partners. Parallel code sharing refers to code sharing between carriers operating on the same route to increase frequency of services and to strengthen market position. Complementary code sharing refers to carriers using each other’s flights to provide connecting services, where they do not offer a full service on their own. The main objective of the complementary code share flights is to increase scope of the partner’s network, allowing them to supply service on markets where they did not operate before. When complementary code shared flights aim at maximizing their combined revenue, it might lead to inequitable distribution of revenue and may cause an alliance partner to lose revenue. In Part II of the thesis, we address this issue of achieving a fair division of the combined revenue generated by the alliance network. The common assumption in revenue sharing methods that are generally practiced is that airline’s valuation of seats in the alliance network is common knowledge. However, in reality it is not true. We therefore consider the valuations of the carriers of their respective products as private information and the price of the product over the entire network to be common knowledge. Under such an information environment, we formulate the problem in the bargaining framework. We discuss the implementation of two solution concepts; namely the Shapley value and the Core of a cooperative game. For the two person cooperative game, the Shapley value equally distributes the surplus among the two parties, while the core allocations of two person cooperative game consists of all possible proportions of the distribution of the surplus. In a bargaining set up, the parties communicate their valuations through sealed bids and agree upon a transfer rule. We analyze two situations. In the first situation we assume that the two parties do not associate any cost towards failure to arrive at an agreement. We determine the optimal bids for the two parties and prove that these optimal bids do not implement any desired point on the core i.e., desired proportion of the distribution of the surplus (which includes the Shapley value).This impossibility result motived the analysis of the second situation, in which we assume that the two parties associate costs towards failure to arrive at an agreement. We once again determine the optimal bids and prove that for a certain structure of the bargaining costs, any desired point on the core, including the Shapley value can be implemented by enticing the players to reveal their true valuations.

Revenue Management (RM) has become one of the most successful application areas of Operation Research. What started off as an obscure practice among few airlines in U.S in early seventies, has attained the status of mainstream business practice, thanks to the major success enjoyed by companies applying RM. Over the same period, academic and industrial research on the methodology of RM has also grown rapidly. Despite the vast technical literature on the subject of revenue management, relatively few papers explicitly model the customer’s choice behavior. Such a behavior of customers could have major impact on revenue realized by an organization. Motivated by this, we focus on addressing the problem faced by a seller who serves customers exhibiting buy-down behavior. We address two important problems faced by a seller with few perishable goods. His objective is to obtain maximum revenue possible by sales of his perishable goods. The seller now potentially faces the problem of fixing the price of the products and then control the availability of products so as to maximize his revenue by minimizing the number of customers who buy-down. The first problem is the multi-product pricing problem where we consider a monop- olistic market situation in which a seller has some quantities of perishable goods under his disposal. The seller has the option of adding few additional features to the base product(perishable good) and thereby differentiating the products to cater to different market segments. Adding each additional feature involves certain cost and there are no restrictions on the availability of the features except that a feature can be added to the base product atmost once . The customers are price-sensitive and the seller is aware of the price-demand relationship of the various customer segments. A customer looking for a product buys the product if and only if the price is less than his reservation price. The sellers’ problem is to identify the price and bundling of features for the various customer segments so as to generate maximum possible revenue. We develop a Mixed integer non-linear mathematical programming model for the problem. We then split the problem into pricing problem and bundling problem and solve them sequentially. We finally provide a numeric example to illustrate the solution procedure. Once the prices are fixed, the next problem is to control the availability of products so as to prevent the buy-down behavior of the customers. We deal with the situation of a seller with two substitutable products. The price of both products are fixed over entire selling period. In a traditional control mechanism structure if the sequence of arrival of customers are known, then it becomes trivial to solve the problem of setting control limits which would prevent buy-down behavior. But in reality it never happens that the seller knows the arrival sequence. Hence in this study to isolate the effect of arrival sequence from other complexities like demand variability, we assume a deterministic demand for both the products but the arrival sequence is randomized. We initially analyze the above described problem and develop a static control mech- anism. We show that the static control mechanism is asymptotically equivalent to the traditional selling mechanism. Then we move on to make modification in the static con- trol mechanism and make it a dynamic control mechanism such that it will respond to the buy-down customers. In order to analyze the performance of dynamic control mechanism, we build a simulation model that would compare traditional selling mechanism and dynamic control mechanism. Statistical analysis is then done on the simulation results. It is shown that for all values of buy-down proportion, on an average the dynamic control mechanism outperforms the traditional control mechanism. Further there is a trend in revenues generated depending upon the buy-down proportion which is also explained. The chapter concludes with operating guidelines for better revenue realization. The organization of the thesis is as follows. In chapter 2, we present the literature survey. We start off with the history of RM and proceed to discuss the inventory control problems in RM in detail. Then we discuss literatures dealing with customer choice behavior. In chapter 3, we define and model the multi - product pricing problem. We present a mixed integer non-linear mathematical program to model the pricing problem. The solution to this problem is divided into two sub problems - the pricing problem and the bundling problem. Solution methodologies for both sub - problem are given and the chapter concludes with a numerical illustration for a 3 - product pricing problem. In chapter 4, we define and address the inventory control problem for a two product case when customers exhibit buy-down nature. We develop a static control mechanism and study its properties. Then we move on to the dynamic control mechanism which would suit real - world conditions. Finally we study the quality of developed methodology using statistical testing methods.