Airlines operate in a multifaceted industry where they employ sophisticated operations research and data analysis techniques to optimize revenue while enhancing customer satisfaction. However, stringent regulations and intense competition in the market make it challenging to increase earnings solely from regular airfare. Consequently, airlines are compelled to rethink their strategies and explore new sources of revenue by unbundling ancillary services from the core airfare. This shift, from initially free ancillary services to chargeable ones, can sometimes lead to passenger dissatisfaction. To mitigate this and rebuild trust, transparent ancillary pricing, and effective policymaking become essential, particularly in service-oriented industries like aviation.

For the first part of the study, we consider the model for a cost-based approach to bring transparency and fairness in baggage ancillary pricing. We employ the concept of efficient cost allocation, drawing from cooperative game theory's Aumann-Shapley (A-S) value, to determine prices for both baggage weight and volume. The concept of A-S value originates from the measure theory where a non-atomic measure is used to allocate the value of the game when the game consists of infinite players. Our methodology extends the traditional A-S value by incorporating chargeable capacity and offering passengers baggage allowances based on weight and volume. Additionally, we develop asymmetric pricing models for different booking periods using a weighted A-S value. Our approach bridges the gap between the traditional Ramsey-Boiteux asymmetric pricing rule and the weighted A-S value. The analysis of a joint-cost function for baggage transport demonstrates that our approach generates higher revenue compared to the existing weight-based pricing system.

The research subsequently explores the complexities involved in crafting baggage allowance policy, shedding light on how baggage services impact airline profitability. We construct a mathematical model that incorporates baggage allowance's impact on passenger and baggage demand. By employing first-order stochastic dominance, we model the stimulating effect of baggage allowance on baggage demand and compute allowance for a pooled demand case. Our findings indicate that the determination of baggage allowance is also influenced by ticket prices, with higher airfares for business class tickets correlating to more generous baggage allowances. In competitive market scenarios, our extended model addresses the joint optimization of airfare and baggage allowance decisions.

The air travel industry accommodates a diverse range of passengers, each with distinct needs and priorities. Leisure travelers, for instance, have preferences distinct from the budget-focused priorities of business travelers, while corporate passengers prioritize service quality and efficiency. Recognizing this diversity, the introduction of itinerary recommendation services emerges as a pivotal factor, significantly reducing search time for corporate travelers and elevating their overall travel experience. The final segment of the research captures this idea. We use the real booking data obtained from Sabre Corporation, a global travel solution provider, to apply advanced machine learning algorithms. The model is tested on the augmented data to compute the choice probability for an itinerary. We explain the features by calculating the relative importance of each feature contributing to the decision-making process. Further, the choice probability is used to rank the itineraries for the Origin-Destination (OD) pair, which can be used as a product for the airline's corporate booking platforms.