<![CDATA[Modern manufacturing industries are increasingly demanding production systems that are both flexible and efficient in handling dynamic operational conditions. Traditional scheduling approaches, such as the job-shop scheduling Problem (JSSP), are limited by the constraint that each operation must be executed on a predefined machine. To address this limitation, the flexible job-shop scheduling Problem (FJSSP) was introduced, allowing alternative machine options for each operation and thereby enhancing system flexibility. This study proposes a scheduling optimization approach based on Multi-Agent Reinforcement Learning (MARL), to support decision-making in complex production environments. Experimental results demonstrate that the proposed method reduces energy consumption by up to 40.62% compared to the First Come First Serve (FCFS) method and by 35.23% compared to the Fastest Available Agent (FAA) method. Moreover, the model shows superior performance in controlling theworst-case makespan and achieves significantly higher success rates in satisfying various production constraints compared to all tested rule-based methods.]]>
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