The airline industry is facing unprecedented challenges in generating sufficient revenues to stay in business. Airlines must capture the greatest revenue yield from every flight by leaving no seats unsold and not over filling the cabin with discount fares. To succeed in doing the above airlines must be able to accurately forecast each of their market segments, manage product andprice availability to maximize revenue and react quickly to competitive changes in the market place. Thus seat inventory control and ticket pricing form the two major tools of revenue management. The focus of this paper is to consolidate the ideas of seats inventory control and pricing in order to maximize the revenues generated by an airline network. A continuous time yield management model for a network with multiple legs, multiple fare classes and dynamic price changes for all fare classes is considered. Each fare class has a set of fares from which the optimal fare is chosen based upon the Minimum Acceptable Fare (MAF) which performs the critical role in the decision process. A machine Learning based algorithm, EMSRa based and EMSRb based algorithm for obtaining dynamic policies for combined pricing and allocation. The algorithms are implemented for a sample network with eight cities, eleven logs, thirty origin-destinations(ODs), three fare classes, three levels of fares in each class and ninety itineraries.

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.

In this thesis, the seat inventory control problem is studied for airlines from the perspective of a risk-averse decision maker. There are only a few studies in the revenue management literature that consider the risk factor. Most of the studies aim at finding the optimal seat allocations while maximizing the expected revenue and do not take the variability of the revenue and hence a risk measure into account. This study aims to decrease the variance of the revenue by increasing the capacity utilization called load factor in the revenue management literature. In addition to expected revenue, load factor is an important performance measure the state companies work with. For this purpose, two types of models with load factor formulations are proposed. This thesis is the first study in the revenue management literature for the airline industry that uses the load factor formulations in the mathematical models. It is an advantage to work with load factor formulations since the models with load factor formulations are much easier to formulate and solve as compared to other risk sensitive models in the literature. The results of the proposed models are evaluated by using simulation for a sample network under different scenarios. The models we propose allow us to control the variability of revenue by changing the used capacity of the aircraft. This is at the expense of a decrease in the revenue under some scenarios. The models we propose perform satisfactorily under all scenarios and they are strongly recommended to be used especially for the small-scale airline companies and state companies and for scheduling new flights even in large scale, well established airline companies.