Havayolu taşımacılığında çok periyodlu dinamik gelir yönetimi ve kapasite optimizasyonu

Air cargo as an industry dealing in across the border shipping of commodities has experienced much success as well as remarkable growth in the recent past, thanks to the evolutionary developments the sector has enjoyed over the past few years. Technically speaking, there would be no profit maximisation by companies offering such services if there was not a perfect technique of selecting shipments to transport. The secret behind ensuring that an air cargo company gets the most out of its services, in terms of profits earned, is a function of its ability to identify the correct shipments to accept as well as the capacity required to spare for each type of cargo amongst those shipped. This study performs a careful analysis in this sector with the aim establishing effective results. The key factors this thesis covers are the design of two different models and their corresponding practical applications.The incoming booking requests for every given time unit are characterised by their capacities, which are in the form of weight and volume and a third parameter of profit rate. These parameters have the sole objective of distributing the available requests in such a manner that maximum profit is earned at the end of the booking period. It is then upon the carriers to make a decision as to whether they should accept or reject the incoming booking requests based on the cargo's capacity, weight, volume and nature. The bid-price mechanism was employed such that whenever the income generated by a given request exceeds its opportunity cost, as indicated by the profit rate, then the request is automatically approved. This procedure is simulated using a dynamic environment. The programme monitors the entire process and summarises the results at the end of every trading season. At this point, the programme selects the most profitable booking requests in terms of weight, volume and profit rate. These selected parameters are then used to calculate the new bid-price, which is then adopted for the subsequent trading period. This way, the subsequent year is characterised by more profitable requests than the previous season. Simulations will be developed to bring an understanding of how such models can be used to achieve the intended objectives. First, the model deals with generating a bid-price mechanism for a single-leg flight such that whenever the income generated by a given request exceeds its opportunity cost as indicated by the profit rate, then the request is automatically approved. Mixed-Integer Non-Linear Programming (MINLP) is developed to calculate dynamic bid-prices that are adjustable at each time unit and solved in a dynamic environment. Bid-prices are calculated closer to departure time when a booking is assumed to be more urgent and charged higher than regularly bookings. However, when a flight is close to its departure time, certain bookings may be accepted and charged regularly to prevent the flight taking off under occupied. Results are compared with static bid-prices, which are calculated once over the decision horizon and results obtained from applying FCFS policy. Second, we suggested adapting a fare-class approach, which is a common technique in passenger revenue management. The customers' requests can only be accepted if there is space in the particular fare class to which they wish to apply. The problem could be approached using the nonlinear programming model (MINLP) hence the revenue function is first linearised before proceeding. The results are compared with the FCFS policy and a booking limit model by introducing dynamic threshold values, which is achieved by limiting the capacity available for shipments in each fare class. Challenges associated with these models will also be discussed to create a clear justification as to why certain methods are recommended and others discouraged. The thesis will end by testing the performance of each of these models. This way, the effectiveness becomes testable and hence the models can be graded.