# Optimizing predictive maintenance and mission assignment to enhance fleet readiness under uncertainty

## TL;DR

This paper introduces a new method to optimize fleet maintenance and mission assignments under uncertainty, improving readiness and efficiency.

## Contribution

The novel FSM model integrates mission assignment, maintenance selection, and uncertainty handling using hybrid reliability assessment.

## Key findings

- The proposed model improves fleet readiness by jointly optimizing mission assignment and maintenance decisions.
- Hybrid reliability assessment using DNNs and analytical models better reflects real-world system behavior.
- Chance-constrained optimization ensures maintenance is completed within break durations with specified confidence.

## Abstract

In many industrial settings, fleets of assets are required to operate through alternating missions and breaks. Fleet Selective Maintenance (FSM) is widely used in such contexts to improve the fleet performance. However, existing FSM models assume that upcoming missions are identical and require only a single system configuration for completion. Additionally, these models typically assume that all missions must be completed, overlooking resource constraints that may prevent readying all systems within the available break duration. This makes mission prioritization and assignment a necessary consideration for the decision-maker. This work proposes a novel FSM model that jointly optimizes system to mission assignment, component and maintenance level selection, and repair task allocation. The proposed framework integrates analytical models for standard components and Deep Neural Networks (DNNs) for sensor-monitored ones, enabling a hybrid reliability assessment approach that better reflects real-world multi-component systems. To account for uncertainties in maintenance and break durations, a chance-constrained optimization model is developed to ensure that maintenance is completed within the available break duration with a specified confidence level. The optimization model is reformulated using two well-known techniques: Sample Average Approximation (SAA) and Conditional Value-at-Risk (CVaR) approximation. A case study of military aircraft fleet maintenance is investigated to demonstrate the accuracy and added value of the proposed approach.