Dynamic Job Shop Scheduling Under Uncertainty Using Genetic Programming

Job shop scheduling(JSS) is a hard problem with most of the research focused on scenarios with the assumption that the shop parameters such as processing times, due dates are constant. But in the real world uncertainty in such parameters is a major issue. In this work, we investigate a genetic programming based hyper-heuristic approach to evolving dispatching rules suitable for dynamic job shop scheduling under uncertainty. We consider uncertainty in processing times and consider multiple job types pertaining to different levels of uncertainty. In particular, we propose an approach to use exponential moving average of the deviations of the processing times in the dispatching rules. We test the performance of the proposed approach under different uncertain scenarios. Our results verified the effectiveness of the newly developed terminal when the level of uncertainty is not high. In addition, the training configuration selection has an important impact on the generalisation of the evolved rules.