Robust Maintenance Scheduling of Aircraft Fleet: A Hybrid Simulation-Optimization Approach

Hani Shahmoradi-Moghadam,Seyed Jafar Sadjadi,Nima Safaei

doi:10.1109/access.2021.3053714

Abstract

We study the maintenance task scheduling problem for an aircraft fleet in an uncertain environment from the viewpoint of robust optimization. Given a daily horizon, the maintenance tasks delegated to a shop should be scheduled in such a way that sufficient aircrafts are available on time to meet the demand of planned missions. The tasks are either scheduled maintenance activities or unexpected repair jobs when a major fault is detected during pre- or after-flight check of each mission. The availability of skilled labour in the shop is the main constraint. We propose a robust formulation so that the maintenance tasks duration is subject to unstructured uncertainty due to the environmental and human factors. As a result of the specific structure of the primary model and non-convexity of the feasible space, the classical robust optimization methods cannot be applied. Thus, we propose an $\epsilon $ -Conservative model in tandem with Monte-Carlo sampling to extract the set of all feasible solutions corresponding to various disturbance vectors. Since the one-way sampling-then-optimization approach does not guarantee the probabilistic feasibility, we employ a hybrid simulation-optimization approach to ensure that the solutions provided by the $\epsilon $ -Conservative model are robust to all uncertainty scenarios. The experimental results confirm the scalability of the proposed methodology by generating the robust optimal solutions, satisfying all conservatism levels and uncertainty scenarios irrespective of the problem size.

Highlights

The maintenance task scheduling problem (MTSP) is an essential challenge at the operational level for many industries
We propose a robust formulation for the aircraft fleet maintenance task scheduling to generate the solution satisfying all conservatism levels and uncertainty scenarios
This paper has presented an approach to robust scheduling of the maintenances tasks of fighter aircraft fleets with daily missions where the tasks’ duration are subject to uncertainty

Summary

INTRODUCTION

The maintenance task scheduling problem (MTSP) is an essential challenge at the operational level for many industries. We propose a robust formulation for the aircraft fleet maintenance task scheduling to generate the solution satisfying all conservatism levels and uncertainty scenarios. Since the primary deterministic model has a specific structure (i.e., uncertain parameters appear in summation bounds) with a non-convex feasible space, the common robust optimization methods, e.g., polyhedral uncertainty [6] or cardinality constrained uncertainty [7], cannot be applied (see Appendix A). To cope with this drawback, our idea is to extract the set of all possible labour scenarios corresponding to various disturbance scenarios using Monte-Carlo sampling.

LITERATURE REVIEW

ROBUST FORMULATION

EXPERIMENTAL RESULTS

CONCLUSION AND REMARKS