Applications of Optimization Methods in Industrial Maintenance Scheduling and Software Testing

As the world is getting more and more competitive, efficiency has become a bigger concern than ever for many businesses. Certain efficiency concerns can naturally be expressed as optimization problems, which is a well studied field in the academia. However, optimization algorithms are not as widely employed in industrial practice as they could. There are various reasons for the lack of widespread adoption. For example, it can be difficult or even impossible for non-experts to formulate a detailed mathematical model of the problem. On the other hand, a scientist usually does not have a deep enough understanding of critical business details, and may fail to capture enough details of the real- world phenomenon of concern. While a model at an arbitrary abstraction level is often good enough to demonstrate the optimization approach, ignoring relevant aspects can easily render the solution impractical for the industry. This is an important problem, because applicability concerns hinder the possible gains that can be achieved by using the academic knowledge in industrial practice. In this thesis, we study the challenges of industrial optimization problems in the form of four case studies at four different companies, in the domains of maintenance schedule optimization and search-based software testing. Working with multiple case studies in different domains allows us to better understand the possible gains and practical challenges in applying optimization methods in an industrial setting. Often there is a need to trade precision for applicability, which is typically very context dependent. Therefore, we compare our results against base values, e.g., results from simpler algorithms or the state of the practice in the given context, where applicable. Even though we cannot claim that optimization methods are applicable in all situations, our work serves as an empirical evidence for the usability of optimization methods for improvements in different industrial contexts. We hope that our work can encourage the adoption of optimization techniques by more industrial practitioners