Research
Forschungsprofil
Research
The research of the team focuses on the development of planning models for the different areas of production, logistics, and supply chain management with a special emphasis on tactical and operational planning using quantitative models. The current trend towards vertically and/or horizontally integrated planning approaches (i.e., the integration of several planning levels as well as the integration of different planning tasks, for example, production and distribution planning) necessitates the development of efficient and robust solution procedures.
Research focus:
Lot-sizing and scheduling, Supply Chain Operations Planning (SCOP), Planning for perishable goods, Robust planning methods considering uncertainty (simulation-based optimization), Development of metaheuristics and hybrid methods (intgeration of exact solution algorithms and metaheuristics) for solving optimization problems
Lot sizing and scheduling are critical components of manufacturing and production planning, focusing on optimizing the quantity and
timing of production runs. Balancing inventory levels and the efforts of setup operations allows to reduce overall operational cost in
production. We are developing advanced mathematical models and cutting-edge algorithms for generating efficient production plans.
Increased data availability, increased computational power, and more efficient solution approaches now allow the integration of
planning tasks at different planning levels or in different domains that had to be solved separately in a traditional approach. Our
research focuses on the modeling aspects of such integrated models, how to solve them, and how to quantify the benefits of such
extended planning approaches.
Even the best organized and managed company faces uncertainty in many ways. Whether it is simply an uncertain demand, a sudden
machine failure, an interruption in the supply chain due to political crises or natural disasters, or the spoilage of perishable goods. We
research methods to deal with these uncertainties and enable companies to be prepared for the unforeseen.
Many planning problems in the supply chain are still too difficult to solve efficiently and quickly. Moreover, each company has its own
specific planning problems. We develop general algorithms and, as far as possible, problem-independent solution methods using
metaheuristics and machine learning algorithms.