In a rapidly changing environment, effective coordination of production and logistics is essential to adapt to evolving technology and fluctuating customer demands while maintaining competitiveness. Job shop scheduling and material flow control are crucial for enhancing productivity and managing costs in manufacturing systems. However, finding optimal solutions often requires significant computational resources, especially for large-scale problems with frequent environmental changes. Material flow control systems must ensure that schedules are adhered to, yet planned production schedules can become ineffective on the shop floor. Given the high automation levels in material flow systems, automated controls must accommodate frequent changes. Despite the interconnection between scheduling and material flow control, these issues are often addressed independently. The complexity increases as both a proper schedule and an effective material flow must be established. This dissertation proposes an integrated approach to combine predictive-reactive job shop scheduling with Programmable Logic Controlled (PLC) material flow. The goal is to create a control system that adapts routing strategies and schedules in response to unexpected events. Utilizing advanced material flow simulation software features, the integrated system comprises a model, a control framework, and a schedule generator. This setup allows for schedule generation, physic
