題 目：Challenges in Air Cargo Revenue Management: recent results and research ideas
主講人：董潤楨 新加坡國立大學
時 間：6月16日下午2:00
地 點：主樓418
主講人簡介：
Prof LC Tang （董潤楨）is the Head of the Department of Industrial and Systems Engineering in the National University of Singapore. He obtained a Ph.D degree from Cornell University in the field of Operations Research with minors in Statistics and Civil Engineering. Dr LC Tang has published more than 70 SCI papers in a variety of international peer-review journals, including European Journal of Operational Research, IIE Transactions, IEEE Transactions on Reliability, Journal of Quality Technology, Naval Research Logistics and Queueing Systems. He is on the editorial review board of the Journal of Quality Technology, the flag-ship journal of the American Society for Quality. He is also an active reviewer for about 30 international journals including Operations Research, Technometric, etc. He has been consulted on problems demanding innovative applications of probability, statistics and other operations research techniques. He is the main author of the book, “Six Sigma: Advanced Tools for BB and MBB” (John Wiley) which was presented with the inaugural Masing Book Prize on the International Academy for Quality in 2007. He is also the co-author of a book on stochastic processes, Markov-Modulated Processes and Semiregenerative Phenomena.
內容簡介：
Revenue management for air cargo is a more challenging problem than that of passengers due to 1) consideration of both weight and size and 2) lack of transparency in the business model. We consider the capacity allocation problem in single-leg air cargo revenue management in which a fraction is reserved for spot market and the remainder for long term allotment. For the spot market, we assume that each cargo booking request is endowed with a random weight, volume and profit rate and propose a Markovian model for the booking request/acceptance/rejection process. The decision on whether to accept the booking request or to reserve the capacity for future bookings follows a bid-price control policy. In particular, a cargo will be accepted only when the revenue from accepting it exceeds the opportunity cost, which is calculated based on bid prices. Optimal solutions are derived by maximizing a reward function of a Markov chain. Finally, inspired by a proposed business model, we discuss some preliminary result for the long term allocation problem.
