Revenue Optimized Capacity Management for Integrators in Air Freight Industry Under Uncertainty

With the increasing adoption of innovative business practices the product lifecycle management is becoming incredibly volatile and challenging. In order to reduce the transportation time many supply chain managers are relying on express air freight based transport services. With the increasing average demand for capacity provided by the air freight service providers, various business entities are appearing in the value chain, such as service providers, integrators. Typically, an integrator owns airbuses to provide service to their clients as well as they can buy extra capacities from other commercial airlines based on need. As per the state of the art practices the capacity for the long term clients are provided through a deterministic model and priced through a bid based mechanism which maximizes the revenue. The demand that realizes close to the time of actual shipment is fulfilled through allocating the capacity through spot market. The spot market capacity allocation is performed based on a popular newsvendor framework. This paper proposes a holistic framework to develop a stochastic model to allocate the capacity for different types of capacity. The objective is to maximize the expected revenue with uncertainties in the system. This problem is fundamentally a dynamic programming problem. We implement an affine controller to develop a computationally tractable formulation of this dynamic programming problem. We conduct experiments from real life data obtained from a global player in this industry to demonstrate the superiority of the proposed model, over the state of the art practices.