Nondifferentiable optimization
Optimization
Modelling and strong linear programs for mixed integer programming
Algorithms and model formulations in mathematical programming
Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
The fleet assignment problem: solving a large-scale integer program
Mathematical Programming: Series A and B
Discrete Mathematics
Solving Large Airline Crew Scheduling Problems: Random Pairing Generation and Strong Branching
Computational Optimization and Applications
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
Itinerary-Based Airline Fleet Assignment
Transportation Science
Airline fleet assignment and schedule design: integrated models and algorithms
Airline fleet assignment and schedule design: integrated models and algorithms
A Bundle Algorithm Approach for the Aircraft Schedule Recovery Problem During Hub Closures
Transportation Science
Airline Schedule Planning: Accomplishments and Opportunities
Manufacturing & Service Operations Management
A Robust Fleet-Assignment Model with Hub Isolation and Short Cycles
Transportation Science
Computational results with a primal-dual subproblem simplex method
Operations Research Letters
A parallel primal-dual simplex algorithm
Operations Research Letters
A least-squares primal-dual algorithm for solving linear programming problems
Operations Research Letters
Integrated Airline Fleet and Crew Robust Planning
Transportation Science
Disruption management in the airline industry-Concepts, models and methods
Computers and Operations Research
A multi-objective approach for robust airline scheduling
Computers and Operations Research
Solving a robust airline crew pairing problem with column generation
Computers and Operations Research
An Optimization Approach to Airline Integrated Recovery
Transportation Science
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Fleet assignment models (FAM) are used by many airlines to assign aircraft to flights in a schedule to maximize profit. Major airlines report that the use of FAM increases annual profits by more than $100 million. The results of FAM affect subsequent planning, marketing, and operational processes within the airline. Anticipating these processes and developing solutions favorable to them can further increase the benefits of FAM. We develop fleet assignment solutions that increase planning flexibility and reduce cost by imposing station purity, limiting the number of fleet types allowed to serve each airport in the schedule. We demonstrate that imposing station purity on the FAM can limit aircraft dispersion in the network and make solutions more robust relative to crew planning, maintenance planning, and operations. Because station purity can significantly degrade computational efficiency, we develop a solution approach, station decomposition, which takes advantage of airline network structure. Station decomposition uses a column generation approach to solving the fleet assignment problem; we further improve the performance of station decomposition by developing a primal-dual method that increases solution quality and model efficiency. Station decomposition solutions can be highly fractional; we develop a “fix-and-price” heuristic to efficiently find integer solutions to the fleet assignment problem. We estimate that the annual net benefit of station purity because of reduced maintenance and crew scheduling costs is greater than $100 million for a major U.S. domestic airline.