# An Integrated Capacity Allocation and Dynamic Pricing Model Designed for Air Cargo Transportation
###### Abstract
investigation, we concentrate on the optimal distribution of capacity between free sales and contractual agreements. [PERSON] [10] provided a solution to the single-leg air cargo revenue management problem. To this end, the authors addressed two distinct problems. Firstly, a decision was made regarding the quantity of cargo capacity that should be contracted. Subsequently, a control policy was devised to regulate spot booking requests from the planning period until the flight takes place. An integer nonlinear model was employed for the initial problem, while a dynamic programming model was developed for the subsequent problem. Our study presents an integrated optimization model that incorporates dynamic pricing, capacity allocation, and demand forecasting as key inputs. Companies want to determine the most appropriate pricing strategies for their products or services. Pricing strategies are inextricably linked with a multitude of factors, including distribution channels, revenue, competition, market demand, and product and service characteristics. Pricing strategies assist businesses in achieving maximum revenue and profit. These strategies should be developed in an analytical and systematic manner in order to align with customer expectations. Although real-time models have been employed in the context of dynamic pricing in air passenger transportation for a considerable period of time, the use of electronic booking and dynamic pricing in the field of air cargo transportation is a relatively recent phenomenon. [PERSON] and [PERSON] [11] assumed that the supplier sells the same product to different markets at different prices. They conducted an integrated study of capacity allocation and dynamic pricing for perishable products. Dynamic pricing in the air cargo industry benefits both the carrier and the customer. Standard freight rates are lower than last-minute sales rates, and if the customer is willing to wait a few extra days, he can obtain transportation at more favorable prices [12]. [PERSON] [13] studied the inventory replenishment and pricing problem by analyzing stochastic demand over multiple periods. [PERSON] and [PERSON] [14] proposed a dynamic pricing model in a multimodal transportation system. [PERSON] [15] proposed a dynamic pricing approach instead of fixed pricing to increase the revenue of multimodal transport operators and created a linear network flow model. The advent of new companies in the sector, augmented capacity through the acquisition of new aircraft or fleet modernization by existing companies, advancements in other modes of transportation, and other factors contribute to heightened competition in the air cargo sector. In an increasingly competitive environment, companies must optimize the utilization of their resources to gain a competitive advantage and enhance profitability. One of the primary considerations in the context of resource utilization within the air cargo sector is the effective management of capacity. It is essential for companies to use their resources more efficiently by making data-driven analyses using historical data and managing the process systematically while allocating capacity. In order to determine the optimal allocation of capacity to be used as an input to the dynamic pricing model, we employed the CVaR and ANN models from the capacity allocation study of [PERSON] and [PERSON] [7]. Another requisite input to the dynamic pricing model is the projected demand for the forthcoming period. In our study, we employed regression analysis, time series analysis, and artificial neural networks (ANNs) to predict this demand. Furthermore, given the numerous factors influencing pricing in the air cargo industry and its inherent dynamism, we opted for a reinforcement learning approach over a supervised learning method, which would have provided pricing recommendations based on historical data.