# Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning

arXiv: https://arxiv.org/abs/2009.03836 · HTML: https://mlsanity.com/paper/2009.03836/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Graph Neural Networks for Job Shop Scheduling Problems: A Survey](https://mlsanity.com/paper/2406.14096/) (2024)
- [Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learning](https://mlsanity.com/paper/2402.00046/) (2024)
- [Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games](https://mlsanity.com/paper/2410.22912/) (2024)
- [Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs](https://mlsanity.com/paper/2604.23841/) (2026)
- [Flexible Manufacturing Systems Intralogistics: Dynamic Optimization of AGVs and Tool Sharing Using Coloured-Timed Petri Nets and Actor-Critic RL with Actions Masking](https://mlsanity.com/paper/2601.04887/) (2026)
- [Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2410.08841/) (2024)
- [Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach](https://mlsanity.com/paper/2411.13366/) (2024)
- [Policy-Based Reinforcement Learning with Action Masking for Dynamic Job Shop Scheduling under Uncertainty: Handling Random Arrivals and Machine Failures](https://mlsanity.com/paper/2601.09293/) (2026)
- [Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems](https://mlsanity.com/paper/2601.11189/) (2026)
- [TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs](https://mlsanity.com/paper/2602.18109/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) is increasingly adopted in job shop scheduling problems (JSSP). But RL for JSSP is usually done using a vectorized representation of machine features as the state space. It has three major problems: (1) the relationship between the machine units and the job sequence is not fully captured, (2) exponential increase in the size of the state space with increasing machines/jobs, and (3) the generalization of the agent to unseen scenarios. We present a novel framework - GraSP-RL, GRAph neural network-based Scheduler for Production planning problems using Reinforcement Learning. It represents JSSP as a graph and trains the RL agent using features extracted using a graph neural network (GNN). While the graph is itself in the non-euclidean space, the features extracted using the GNNs provide a rich encoding of the current production state in the euclidean space, which is then used by the RL agent to select the next job. Further, we cast the scheduling problem as a decentralized optimization problem in which the learning agent is assigned to all the production units and the agent learns asynchronously from the data collected on all the production units. The GraSP-RL is then applied to a complex injection molding production environment with 30 jobs and 4 machines. The task is to minimize the makespan of the production plan. The schedule planned by GraSP-RL is then compared and analyzed with a priority dispatch rule algorithm like first-in-first-out (FIFO) and metaheuristics like tabu search (TS) and genetic algorithm (GA). The proposed GraSP-RL outperforms the FIFO, TS, and GA for the trained task of planning 30 jobs in JSSP. We further test the generalization capability of the trained agent on two different problem classes: Open shop system (OSS) and Reactive JSSP (RJSSP) where our method produces results better than FIFO and comparable results to TS and GA.