Airline disruption management: dynamic aircraft scheduling with ant colony optimization

Disruption management is one of the main concerns of any airline company, as it can influence its annualrevenue by upwards of 3%. Most of medium to large airlines have specialized teams which focus onrecovering disrupted schedules with very little automation. This paper presents a new automated approachto solve both the Aircraft Assignment Problem (AAP) and the Aircraft Recovering Problem (ARP), wherethe solutions are responsive to unforeseen events. The developed algorithm, based on Ant ColonyOptimization, aims to minimize the operational costs involved and is designed to schedule and rescheduleflights dynamically by using a sliding window. Test results tend to indicate that this approach is feasible,both in terms of time and quality of the proposed solutions

Replanning in Predictive-reactive Scheduling

Abstract Achieving optimal results in real-life production scheduling is precluded by a number of problems. One such problem is dynamics of environments with unavailable resources (such as machine breakdowns and ill workers) and new demands (e.g. new orders) coming during the schedule execution. Traditional approach to react to unexpected events occurring on the shop floor is generating a new schedule from scratch. Complete rescheduling, however, may require excessive computation time. Moreover, the recovered schedule may deviate a lot from the ongoing schedule. Some work has focused on tackling these shortcomings, but none of the existing approaches tries to substitute jobs that cannot be executed with a set of alternative jobs. This paper reviews techniques related to predictive-reactive scheduling and suggests the future goal, which is to propose algorithms for dealing with unexpected events using the possibility of alternative processes