Modern manufacturing environments frequently experience disruptions such as urgent orders, resource failures, and varying resource availability. This paper addresses these challenges in the dynamic Flexible Job Shop Scheduling Problem (FJSP) by extending an existing reinforcement learning model to accommodate real-time production changes. The initial sections examine the original FJSP model, emphasizing its core constraints and optimization methodology. Subsequently, newly introduced constraints such as additional job arrivals and resource breakdowns are incorporated into the baseline formulation. The resulting framework continually revises resource allocations and operation sequences in response to these events, ensuring efficient and feasible scheduling. This work demonstrates a practical strategy for adapting static approaches to dynamic contexts, thereby supporting more resilient and real-time decision-making in modern flexible manufacturing systems.