Graduation Year


Document Type




Degree Granting Department

Industrial Engineering

Major Professor

Tapas K. Das, Ph.D.

Committee Member

Kandethody M. Ramachandran, Ph.D.

Committee Member

Ali Yalchin, Ph.D.


Dynamic pricing, Heuristics, Optimization, Reinforcement learning, Seat inventory control


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.