# An Integrated Capacity Allocation and Dynamic Pricing Model Designed for Air Cargo Transportation
###### Abstract
We aimed to learn from the environment with the reward mechanism we determined with reinforcement learning, which does not require human supervision for problem solving, does not require labeling of data, and does not require correction of inappropriate actions. Our model was constructed using data from one of the industry's foremost air cargo carriers, and the ensuing results were subjected to evaluation. To address the complexities inherent in the process, we sought the insights of industry experts and conducted a comprehensive review of the extant literature. Table 1 presents a comprehensive list of the experts who contributed to the study. In order to gain a deeper comprehension of the pricing dynamics in the air cargo industry, it is imperative to identify the key parameters that influence pricing and to consider the distinctive attributes of individual air cargo companies. In the absence of dynamic pricing, the manual review and determination of prices at specified intervals results in the loss of both labor and the potential for increased profitability. Giving the right prices to products through dynamic pricing will help increase the airline's revenue. Furthermore, the proposed dynamic pricing model is integrated with the outputs of the capacity allocation and demand forecasting models, which are designed to ensure optimal capacity allocation. Ultimately, the output of the dynamic pricing model serves as the input for the capacity allocation model in subsequent periods. The proposed model is solved using real data from a global air cargo company that stands out in air cargo transportation and has a large fleet and a wide network, thus providing a practical example of its functionality. In this context, our model differs from existing literature in that it is an integrated structure, and thus contributes to both the development of academic knowledge and the field of aviation literature. Table 2 presents a comparative analysis of the studies under review and our proposed models. The second section of the paper delineates the general structure of the proposed model, provides a discussion of the model's assumptions, and offers a summary of the case studies. The final section of the paper, Section 3, comprises two separate subsections. The first subsection addresses the generalizability of the methodology, and the second subsection presents prospective research ideas for future consideration. We can summarize the main contribution of our study as;  
Integrated Model Structure: An integrated decision support model is presented where capacity allocation, demand forecasting, and dynamic pricing processes are designed in mutual interaction. This structure provides system integrity by taking into account the dynamic relationships between decision variables. Input Set Supported by Expert Opinions: In determining the model inputs, not only the literature but also the opinions of sectoral experts were taken into account, and a structure with high practical validity was created. Methodological Diversity and Compatible Modeling: The most appropriate methods for different problem components are determined and integrated with each other in a compatible manner. The use of ANN for capacity allocation, regression analysis for demand \begin{table}
\begin{tabular}{c c c c} \hline
**Expert** & **Expertise Area** & **Title** & **Experience (Years)** \\ \hline E1 & Sales & Regional Manager & 10 \\ E2 & Sales & Regional Manager & 12 \\ E3 & Sales & Regional Manager & 9 \\ E4 & Strategy & Manager & 20+ \\ E5 & Strategy & Supervisor & 10 \\ E6 & Marketing & Specialist & 8 \\ E7 & Product & Supervisor & 9 \\ E8 & Operation & Supervisor & 10 \\ E9 & Operation & Specialist & 10 \\ E10 & Quality & Manager & 9 \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the experts. forecasting, and the SARSA algorithm for pricing provides methodological diversity and modeling depth.