A Simulated Annealing Hyper-heuristic for Job Shop Scheduling Problems

Job Shop Scheduling problems (JSSPs) have become increasingly popular due to their application in supply chain systems. Several solution approaches have appeared in the literature. One of them is the use of low-level heuristics. These methods approximate a solution but only work well on some kind of problems. Hence, combining them may improve performance. In this paper, we use the classical stochastic local optimization algorithm Simulated Annealing to train a selection hyper-heuristic for solving JSSPs. To do so, we use an instance generator provided in literature to create training sets with a different number of instances: 20, 40, and 60. In addition, we select instances from the literature to create two test scenarios, one similar to the training instances, and another with bigger problems. Our results suggest that training with the highest number of instances lead to better and more stable hyper-heuristics. For example, in the first test scenario, we achieved a reduction in the data range of over 60% and an improvement in the median performance of almost 30%. Moreover, under these conditions about 75% of the generated hyper-heuristics were able to perform equal to or better than the best heuristic. Even so, less than 25% were able to outperform the synthetic Oracle. Because of the aforementioned, we strongly support the idea of using a selection hyper-heuristic model powered by Simulated Annealing for creating a high-level solver for Job Shop Scheduling problems.