Job Shop Scheduling

Job shop scheduling (JSSP) is a complex optimization problem focused on efficiently assigning tasks to machines to minimize overall production time and resource usage. Current research heavily utilizes machine learning, particularly reinforcement learning (RL) and graph neural networks (GNNs), often integrated with techniques like constraint programming and local search to improve solution quality and scalability. These advancements aim to address the NP-hard nature of JSSP, offering significant potential for optimizing manufacturing processes and improving efficiency across various industries. The development of robust benchmarks and publicly available datasets further facilitates progress and comparison of different approaches.