Scenario-based stochastic programming for an airline-driven flight rescheduling problem under ground delay programs

We address an airline-driven flight rescheduling problem within a single airport in which a series of ground delay programs (GDPs) are considered. The objective of the problem is to minimize an airline’s total relevant cost (TRC) consisting of delay costs, misconnection costs, and cancellation costs that would result from flight rescheduling. We introduce three solution approaches—the greedy approach, the stochastic approach, and the min-max approach—that revise the daily flight scheduling whenever the schedule is affected by a GDP or further GDP changes. The greedy approach simply searches for a solution using currently updated static GDP information, and the other two approaches provide a solution by considering possible scenarios for changes of the GDP. Using real-world data in existing literature and some generated scenarios, we present extensive computational results to assess the performance of the approaches. We also report the values of information on GDP the solution approaches refer to. Deliberating various cost parameter settings an airline might consider, we discuss the value of information in implementing the proposed solution approaches.