Flexible Job Shop Scheduling Problem

The Flexible Job Shop Scheduling Problem (FJSP) focuses on optimizing the assignment of jobs to machines and their sequencing to minimize metrics like makespan (total completion time) in flexible manufacturing environments. Current research heavily emphasizes the use of deep reinforcement learning (DRL), often coupled with constraint programming or graph neural networks, to address the computational complexity of FJSP, particularly for large-scale instances and those incorporating transportation constraints or human factors. These advanced techniques aim to improve upon traditional methods like dispatching rules and metaheuristics, leading to more efficient and robust scheduling solutions. The advancements in FJSP research have significant implications for optimizing production processes in various industries, enhancing resource utilization, and improving overall manufacturing efficiency.