The fleet assignment problem: solving a large-scale integer program
Mathematical Programming: Series A and B
Daily aircraft routing and scheduling
Management Science
A Stochastic Model of Airline Operations
Transportation Science
Runway Operations Planning and Control - Sequencing and Scheduling
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Rerouting Aircraft for Airline Recovery
Transportation Science
Operational airline reserve crew planning
Journal of Scheduling
MEANS—MIT Extensible Air Network Simulation
Simulation
Disruption management in the airline industry-Concepts, models and methods
Computers and Operations Research
Constraint-specific recovery network for solving airline recovery problems
Computers and Operations Research
Recovering from Airline Operational Problems with a Multi-Agent System: A Case Study
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Operational problems recovery in airlines: a specialized methodologies approach
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
An Optimization Approach to Airline Integrated Recovery
Transportation Science
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The sources of disruption to airline schedules are many, including crew absences, mechanical failures, and bad weather. When these unexpected events occur, airlines recover by replanning their operations. In this paper, we present airline schedule recovery models and algorithms that simultaneously develop recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. The objective is to minimize jointly airline operating costs and estimated passenger delay and disruption costs. This objective works to balance these costs, potentially increasing customer retention and loyalty, and improving airline profitability.Using an Airline Operations Control simulator that we have developed, we simulate several days of operations, using passenger and flight information from a major US airline. We demonstrate that our decision models can be applied in a real-time decision-making environment, and that decisions from our models can potentially reduce passenger arrival delays noticeably, without increasing operating costs.