Solving large scale industrial production scheduling problems with complex constraints: an overview of the state-of-the-art

Production scheduling is challenging and the body of literature addressing various variants of the problem is large. It can roughly be divided into two streams: The first stream addresses and generalizes established scheduling problems, being general in the sense that they are not only applicable in a particular industry. The second stream works on less generic scheduling approaches for real industry cases by enriching standard models with all the required realistic aspects, such as process overlapping or sequence dependent setup times. Furthermore, different approaches have different limitations in terms of the problem size that they can tackle. The rise of Industry 4.0 has lead to a significant increase in data collection activities and the gathered information is used to build larger and more complex models. Industrial use cases may consist of several thousand operations on a large variety of machines, while classical benchmark instances tend to range up to only a few hundred of operations. It is therefore necessary to identify and highlight approaches, that can meet the challenges of scheduling in the era of Industry 4.0 and are suitable to tackle large scale problems.