Hava kargo taşımacılığında kapasite ataması ve dinamik fiyatlandırma

The air cargo industry is of significant importance to the global economy, employing a distinctive business model that makes it the most preferred mode of transportation, particularly for the rapid delivery of high-value goods. Airlines are constrained by limited cargo capacity, which presents a significant challenge for the industry. Maximizing revenue by selling capacity to the most profitable customers at the most advantageous price is an important issue that directly affects profitability. This study addresses the issue of capacity allocation, demand forecasting and dynamic pricing for air cargo firms. It is divided into three steps: the first is the allocation of capacity to allotment sales, the second involves the development of a demand-forecasting model that considers capacity excluding pre-sales, while the third stage entails the creation of a dynamic pricing model using the aforementioned forecast as an input. The unit revenue recommendations obtained from the dynamic pricing model were integrated to feed the capacity allocation model, which is the first stage of the study. Air cargo companies have limited capacity and wish to utilize it optimally. It is possible to pre-sell capacity through agreements or to make it available for future demand. The proportion of total capacity allocated to deals is crucial for more efficient capacity utilization. In the first stage of the problem, a model is proposed for the optimum allocation of capacity. The capacity solution to the problem is formulated with CVaR and ANN models, and the outputs of the models are then compared. The model that yields the optimal result is selected. Subsequently, a demand forecasting model is constructed, incorporating capacity as input. Regression analysis, time series methods, and ANNs were employed for demand forecasting. The values produced by the model were compared with the actual values, and the model that gave the best result was included in the solution. Due to the multitude of variables influencing the price in the air cargo sector and the intricate structure of the sector, it is exceedingly challenging and complex to determine the price dynamically in air cargo. To circumvent the difficulties inherent in this complexity, in addition to a comprehensive literature review, the opinions of industry experts were sought and the model inputs were determined. The problem solution was completed by employing SARSA algorithm for dynamic pricing. The study is concluded by evaluating the solutions and making suggestions for future studies.