# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 2 Related Works
The study employs a Multi-Policy Proximal Policy Optimization (MPPPO) algorithm with a co-evolution mechanism to simultaneously optimize makespan and total tardiness. The approach leverages multiple policy networks to generate diverse Pareto-optimal solutions, guided by an adaptive reward mechanism. Comparative analyses show that MPPPO outperforms traditional dispatching rules and other DRL-based methods in terms of efficiency, convergence, and diversity, providing robust solutions for complex scheduling scenarios. However, while deep learning and RL approaches can offer flexible, data-driven solutions, they often require large amounts of training data and computational resources, making them less practical for smaller-scale problems or environments with limited data availability. Additionally, these methods may struggle to incorporate strict task dependencies and constraints without significant modifications to the learning algorithms. In contrast, our proposed constraint programming approach does not rely on extensive data or require lengthy training phases, making it more suitable for structured, deterministic MRO environments where real-time data analysis is a primary focus. Operational research (OR) tools have been widely applied in the aviation industry to tackle complex scheduling problems [9]. A lookahead approximate dynamic programming (ADP) methodology was introduced to optimize aircraft maintenance check scheduling under uncertainty, achieving a significant reduction in maintenance slots and improving aircraft availability [10]. While ADP and other OR-based approaches are effective for handling large-scale optimization problems with stochastic elements, they often rely on approximations and may not guarantee truly optimal solutions. Our study differs from these approaches by utilizing constraint programming, which focuses on deterministic optimization. The key advantage of constraint programming in this context is its ability to find exact solutions in cases where the problem size is manageable, such as the MRO task scheduling problem presented in this paper. This contrasts with the approximate nature of ADP and other OR methods, which may not always provide the most efficient schedule. While prior studies have made significant advances in optimizing MRO scheduling through heuristic, machine learning, and operational research methods, each of these approaches has limitations in handling dynamic and unpredictable MRO environments or in balancing the trade-offs between solution quality and computational efficiency. Our proposed constraint programming model addresses these gaps by focusing on a structured optimization approach that leverages task dependencies, resource constraints, and workplace allocation to achieve high scheduling efficiency and resource utilization. Compared to heuristic and deep learning-based models, our constraint programming approach offers a more precise solution in a reasonable time frame without requiring extensive computational resources. By using maintenance log data obtained from Korean Air's MRO, we demonstrate that this method can achieve superior resource utilization (up to 100% in some cases) and provide near-optimal solutions for small-to-medium-sized MRO scheduling problems, where real-time data analysis is required.