Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the application of graph neural networks (GNNs) to solve JSSPs, albeit lacking a systematic survey of the relevant literature. This paper aims to thoroughly review prevailing GNN methods for different types of JSSPs and the closely related flow-shop scheduling problems (FSPs), especially those leveraging deep reinforcement learning (DRL). We begin by presenting the graph representations of various JSSPs, followed by an introduction to the most commonly used GNN architectures. We then review current GNN-based methods for each problem type, highlighting key technical elements such as graph representations, GNN architectures, GNN tasks, and training algorithms. Finally, we summarize and analyze the advantages and limitations of GNNs in solving JSSPs and provide potential future research opportunities. We hope this survey can motivate and inspire innovative approaches for more powerful GNN-based approaches in tackling JSSPs and other scheduling problems.

We introduce an open-source GitHub repository containing comprehensive benchmarks for a wide range of machine scheduling problems, including Job Shop Scheduling (JSP), Flow Shop Scheduling (FSP), Flexible Job Shop Scheduling (FJSP), FJSP with Assembly constraints (FAJSP), FJSP with Sequence-Dependent Setup Times (FJSP-SDST), and the online FJSP (with online job arrivals). Our primary goal is to provide a centralized hub for researchers, practitioners, and enthusiasts interested in tackling machine scheduling challenges.

The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving complex combinatorial optimization problems (COPs). ALNS selects various heuristics adaptively during the search process, leveraging their strengths to find good solutions for optimization problems. However, the effectiveness of ALNS depends on the proper configuration of its selection and acceptance parameters. To address this limitation, we propose a Deep Reinforcement Learning (DRL) approach that selects heuristics, adjusts parameters, and controls the acceptance criteria during the search process. The proposed method aims to learn, based on the state of the search, how to configure the next iteration of the ALNS to obtain good solutions to the underlying optimization problem. We evaluate the proposed method on a time-dependent orienteering problem with stochastic weights and time windows, used in an IJCAI competition. The results show that our approach outperforms vanilla ALNS and ALNS tuned with Bayesian Optimization. In addition, it obtained better solutions than two state-of-the-art DRL approaches, which are the winning methods of the competition, with much fewer observations required for training. The implementation of our approach will be made publicly available.

Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good solutions. However, this parameter tuning could be very computationally expensive in solving non-trial (combinatorial) optimization problems. This paper proposes a framework that integrates MOEAs with adaptive parameter control using Deep Reinforcement Learning (DRL). The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization. We test the proposed approach with a simple benchmark problem and a real-world, complex warehouse design and control problem. The experimental results demonstrate the advantages of our method in terms of solution quality and computation time to reach good solutions. In addition, we show the learned policy is transferable, i.e., the policy trained on a simple benchmark problem can be directly applied to solve the complex warehouse optimization problem, effectively, without the need for retraining.