Network Cargo Capacity Management

Authors:
Tatsiana Levina;Yuri Levin;Jeff McGill;Mikhail Nediak
Affiliations:
School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada;School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada;School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada;School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada
Venue:
Operations Research
Year:
2011

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Abstract

We consider the problem faced by an airline that is flying both passengers and cargo over a network of locations on a fixed periodic schedule. Bookings for many classes of cargo shipments between origin-destination pairs in this network are made in advance, but the weight and volume of aircraft capacity available for cargo as well as the exact weight and volume of each shipment are not known at the time of booking. The problem is to control cargo accept/reject decisions to maximize expected profits while ensuring effective dispatch of accepted shipments through the network. This network stochastic dynamic control problem has very high computational complexity. We propose a linear programming and stochastic simulation-based computational method for learning approximate control policies and discuss their structural properties. The proposed method is flexible and can utilize historical booking data as well as decisions generated by default control policies.