Column generation for airline problems

Issued as Progress report, and Final project report, Project no. E-24-66

A deep cut ellipsoid algorithm for convex programming

This paper proposes a deep cut version of the ellipsoid algorithm for solving a general class of continuous convex programming problems. In each step the algorithm does not require more computational effort to construct these deep cuts than its corresponding central cut version. Rules that prevent some of the numerical instabilities and theoretical drawbacks usually associated with the algorithm are also provided. Moreover, for a large class of convex programs a simple proof of its rate of convergence is given and the relation with previously known results is discussed. Finally some computational results of the deep and central cut version of the algorithm applied to a min—max stochastic queue location problem are reported

Integer Programming: Global Impact

Presented on November 25, 2013 from 11:00 am to 12:00 pm instructional center of the ISyE Main Building.Runtime: 59:12 minutes.Integer programming is the (not very appealing or descriptive) name for optimization models and algorithms in which some variables are required to have integer values. Planning and operational problems in energy, finance, health, manufacturing, military, transportation, and in almost any imaginable domain where decisions are made, are formulated and solved using integer programming. For example, most Fortune 500 companies use integer programming in some aspects of their business. Currently available software is capable of solving models with thousands, and sometimes millions, of variables and constraints. We will discuss some integer programming models whose solutions have had big impact in solving important problems, and present recent progress that has made it possible to solve very large instances and to obtain provably good solutions quickly. We'll close by speculating on future advances in methodology and applications. Integer programming is the (not very appealing or descriptive) name for optimization models and algorithms in which some variables are required to have integer values. Planning and operational problems in energy, finance, health, manufacturing, military, transportation, and in almost any imaginable domain where decisions are made, are formulated and solved using integer programming. For example, most Fortune 500 companies use integer programming in some aspects of their business. Currently available software is capable of solving models with thousands, and sometimes millions, of variables and constraints. We will discuss some integer programming models whose solutions have had big impact in solving important problems, and present recent progress that has made it possible to solve very large instances and to obtain provably good solutions quickly. We'll close by speculating on future advances in methodology and applications

Sports Scheduling

Presentation given in the Wilby Room of the Georgia Tech Library and Information Center.Runtime: 70:47 minutesCollege and professional sports, including basketball, baseball, football and hockey, is a multi-billion dollar industry with a substantial part of the revenue derived from television. To maximize revenue, it is crucial to have important games televised on the right days and times. These requirements frequently conflict with more traditional requirements of a "fair" schedule that balances strength of schedule, home and away games, and travel. Sports scheduling can be thought of as the engineering of the sports entertainment supply chain. A typical model for a sports scheduling problem is a combinatorial design with nasty side constraints and multi-objectives. In this talk we discuss our experience with the Sports Scheduling Group in scheduling ACC basketball and football, and major league baseball

Deployment of the MINTO mixed-integer optimization

Issued as Final report, Project E-24-X6