Job Shop Scheduling Problem

The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization challenge focused on efficiently assigning jobs to machines to minimize criteria like makespan (total completion time). Current research heavily utilizes deep reinforcement learning (DRL), often coupled with graph neural networks (GNNs) or integrated with constraint programming, to develop adaptable and scalable scheduling solutions. This focus stems from the limitations of traditional methods in handling real-world complexities like machine setups, variable batch sizes, and uncertain task durations, with significant implications for optimizing manufacturing processes and resource allocation across various industries.