Optimization Applications in Air Transportation

MD34.1 Class Scheduling for Pilot Training

• Jonathan Bard; University of Texas, Dept. of Mech. Eng., Austin, TX 78712-1175; jbard@mail.utexas.edu
• Xiangtong Qi; University of Texas, MSIS Dept., McCombs Sch. of Bus., Austin, TX 78712; qix@mail.utexas.edu
• Gang Yu; University of Texas, Dept. of MSIS, McCombs Sch. of Bus., Austin, TX 78712; yu@uts.cc.utexas.edu

We study the pilot training class scheduling problem for a major airline. Different from most timetabling models, this is a class scheduling problem over a long time period. We will present the complexity, modeling, solution techniques as well as computational results for this problem.

MD34.2 Strong Formulations in Airline Schedule Design & Fleet Assignment

• Amr A. Farahat; MIT, OR Ctr., 77 Massachusetts Ave., Rm. E40-130, Cambridge, MA 02139; afarahat@mit.edu
• Cynthia Barnhart; MIT, 77 Massachusetts Ave., Rm. 1-229, Cambridge, MA 02139; cbarnhar@mit.edu
• Manoj Lohatepanont; American Airlines; manoj.lohatepanont@aa.com

We address the problem of determining flight times and fleet types to assign to an airline's flight network. These problems can be modeled as huge mixed-integer programs whose solution are computationally challenging. We present new formulations with tight LP relaxations and provide preliminary results based on data drawn from a major US airline.

MD34.3 Aircraft Routing under Weather Uncertainty

• Arnab Nilim; University of California, Dept. of EECS, 262M Cory Hall, Berlekey, CA 94720; nilim@robotics.eecs.berkeley.edu
• Mark Hansen; University of California, NEXTOR, Civil/Environ. Eng., 107B McLaughlin Hall, ITS, Berkeley, CA 94270; mhansen@newton.me.berkeley.edu
• Laurent El Ghaoui; University of California, Dept. of EECS, 262M Cory Hall, Berkeley, CA 94720; elghaoui@eecs.berkeley.edu
• Vu Duong; Euro Control, Ctr. Experimental Eurocontrol, Bretigny s-Orge, 15-91222 , France; vu.duong@eurocontrol.fr

Convective weather causes delay and disruption in the national airspace system, in part because of the difficulty in forecasting it. We consider the problem of optimal aircraft routing under weather uncertainty. Our approach employs Markov decision processes and a dynamic programming algorithm that will provide a routing strategy that minimizes the expected delay.