A model enhancement heuristic for building robust aircraft maintenance personnel rosters with stochastic constraints

• We solve a personnel planning problem with stochastic delays in demand arrival times.
• A model enhancement algorithm is proposed that combines simulation and optimization.
• Our model can ensure a specified service level when stochasticity is introduced.

This paper presents a heuristic approach to optimize staffing and scheduling at an aircraft maintenance company. The goal is to build robust aircraft maintenance personnel rosters that can achieve a certain service level while minimizing the total labor costs. Robust personnel rosters are rosters that can handle delays associated with stochastic flight arrival times. To deal with this stochasticity, a model enhancement algorithm is proposed that iteratively adjusts a mixed integer linear programming (MILP) model to a stochastic environment based on simulation results. We illustrate the performance of the algorithm with a computational experiment based on real life data of a large aircraft maintenance company located at Brussels Airport in Belgium. The obtained results are compared to deterministic optimization and straightforward optimization. Experiments demonstrate that our model can ensure a certain desired service level with an acceptable increase in labor costs when stochasticity is introduced in the aircraft arrival times.