Efficient master production scheduling for manufacturing systems using an enhanced SARSA algorithm

Efficient master production scheduling is crucial in production planning, yet automated solutions remain scarce. This paper introduces a novel scheduling method, master production scheduling with improved state-action-reward-state-action (MPS-ISARSA) algorithm, based on an enhanced state-action-reward-state-action (SARSA) framework. Using a Markov decision process model, the method optimises scheduling through innovative reward shaping and a linearly decaying epsilon-greedy (εlin-greedy) policy to accelerate training. An overtime penalty ensures alignment between production capacity and demand. Extensive simulations validate the model's effectiveness, demonstrating superior convergence and efficiency compared to traditional methods and other reinforcement learning algorithms. This approach offers a scalable, intelligent framework for capacity-constrained production scheduling, with practical applications for manufacturing industries aiming to enhance operational efficiency.