We consider the handling of baggage from passengers changing aircraft at an airport. The transfer baggage problem is to assign the bags from each arriving aircraft to an infeed area, from where a network of conveyor belts will bring them to the corresponding outbound flight. The main objective is to minimize the number of missed bags, but in order to reach this goal, we introduce some auxiliary objectives that minimize the transportation time of bags, while ensuring robustness and avoiding overload of the handling facilities. We first present a static mixed integer programming model for the transfer baggage problem. However, the transfer baggage process is subject to uncertainty related to aircraft arrival time, transportation time from aircraft to baggage handling facility, and capacity use in the baggage handling system. In order to handle this uncertainty, a stochastic model is developed, optimizing over a finite set of scenarios. Although the model in theory should lead to more balanced and stable decisions, it suffers of long solution times. Therefore, a semi-stochastic model is presented, where short-term decisions are stochastic, while long-term decisions are deterministic. Computational experiments on real-life data from a major European hub airport are reported, demonstrating that each of the three models has its advantages. In particular, the semi-stochastic model shows promising results both with respect to robustness and solution times.
- Airport baggage handling
- Optimization under uncertainty
- Robust optimization
- Stochastic programming