In this paper we present a solution methodology based on the stochastic branch and bound
algorithm to find optimal, or close to optimal, solutions to the stochastic airport runway
scheduling problem. The objective of the scheduling problem is to find a sequence of
aircraft operations on one or several runways that minimizes the total makespan, given
uncertain aircraft availability at the runway. Enhancements to the general stochastic
branch and bound algorithm are proposed and we give the specific details pertaining to
runway scheduling. We show how the algorithm can be terminated early with solutions
that are close to optimal, and investigate the impact of the uncertainty level. The
computational experiment indicates that the sequences obtained using the stochastic
branch and bound algorithm have, on average, 5–7% shorter makespans than sequences
obtained using deterministic sequencing models. In addition, the proposed algorithm is
able to solve instances with 14 aircraft using less than 1 min of computation time
In this paper we present a solution methodology based on the stochastic branch and boundalgorithm to find optimal, or close to optimal, solutions to the stochastic airport runwayscheduling problem. The objective of the scheduling problem is to find a sequence ofaircraft operations on one or several runways that minimizes the total makespan, givenuncertain aircraft availability at the runway. Enhancements to the general stochasticbranch and bound algorithm are proposed and we give the specific details pertaining torunway scheduling. We show how the algorithm can be terminated early with solutionsthat are close to optimal, and investigate the impact of the uncertainty level. Thecomputational experiment indicates that the sequences obtained using the stochasticbranch and bound algorithm have, on average, 5–7% shorter makespans than sequencesobtained using deterministic sequencing models. In addition, the proposed algorithm isable to solve instances with 14 aircraft using less than 1 min of computation time
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