Optimization of Airport Surface Operations Under Uncertainty

Extended Aircraft Arrival Management Under Uncertainty: A Computational Study

“…Although a well-applied SAA method can guarantee good-quality solutions for the original problem, computing times are very often inappropriate for real-time implementation. Consequently, fast solution methods based on SAA were proposed in the literature related to aircraft scheduling using stochastic programming [11,13].…”

“…The case in which predicted operations times are known with certainty, called the deterministic case, has been thoroughly studied in the literature [4,7,8], while the case under uncertainty has less often been addressed. So far in the related literature, three main approaches to optimization under uncertainty were applied to ASSP: probabilistic [9,10], stochastic [11][12][13] and robust [14][15][16] approaches. Pioneer studies such as [9,10] mainly enriched deterministic models by probability constraints and/or by a probability objective-value function.…”

“…Pioneer studies such as [9,10] mainly enriched deterministic models by probability constraints and/or by a probability objective-value function. Stochastic programming models, including two-stage and multi-stage models, were proposed in [11][12][13].…”

“…Since solving a single SAA problem with a large scenario set may be time-consuming, [11,13] opt for solving several replications of an SAA problem with a limited number of scenarios. Upon optimization, they are left with a pool of near-optimal solutions to the original problem (as many solutions as replications).…”

“…Distinct solutions are kept in a pool from which only one solution need to be selected at the end. On one hand, Bosson and Sun [13] simply select the solution with the minimum objective function value. Hence, no post-optimization validation is performed.…”