SGER: Air-Cargo Revenue Management in the Presence of Intermediaries

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

This grant provides funding for the development of mathematical models for maximizing airlines' long-run revenue from cargo operations. Two types of models will be developed. The short-term models will focus on the airlines' decisions about whether or not to accept each booking request from direct-ship customers. Booking decisions are made dynamically based on current capacity commitments, historical booking profile, and time remaining until departure. Such decisions must also explicitly consider weight and volume dimensions of the package and capacity commitments to freight forwarders who, acting as independent intermediaries, pre-purchase cargo capacity and generate additional streams of demand by performing pick-up, storage, customs' clearance, packing, and local delivery to consignees. The medium-term models will focus on the airlines' decisions about which combinations of capacity and price to offer for sale to the freight forwarders. Both short and medium term problems will be formulated as Markov Decision Processes and structural properties of the optimal solutions will be identified, where possible. Algorithms that generate good heuristic solutions and bounds on the airline's optimal expected revenue will be developed and their accuracy will be tested through extensive numerical experiments. If successful, the results of this research may affect the way in which airlines think about and manage cargo capacity. Since air-cargo revenue management is an uncharted area for academic researchers, the proposed work will also open up new avenues for future investigation. It will lead to the development of new models and computational techniques for bounding and testing heuristic solutions to large scale stochastic optimization problems. Another goal of this research is to demonstrate the potential benefits of using stochastic optimization based solutions for managing cargo capacity and to generate new managerial insights. Finally, the proposed work will contribute to the training of graduate and undergraduate students.