Towards a Systematic Development Process of Optimization Methods
This addresses inefficiencies in collaborative optimization projects, but it is incremental as it builds on existing interdisciplinary practices.
The paper tackles the problem of communication errors in interdisciplinary teams developing optimization methods, proposing tools like the algorithm engineering cycle and checklists to avoid and repair these errors, with examples from continuous optimization.
The ultimate goal of all optimization methods is to solve real-world problems. For a successful project execution, knowledge about optimization and the application has to be pooled. As it is too inefficient to highly train one person in both fields, a team of experts is usually put together. Unfortunately, communication errors must be expected when several people collaborate. In this work, we deal with the avoidance and the repair of these communication errors. The tools proposed in this regard are, among others, the algorithm engineering cycle, checklists for structuring communication, and knowledge databases. The discussion is enriched with examples from continuous optimization, but most tools have a much wider applicability, even beyond optimization.