Advanced Optimization Methods (Master)

• Selected state-of-the-art metaheuristics (e.g., adaptive large neighborhood search)
• Thoroughly go through its implementation (e.g., initial solution, types of subheuristics, neighborhood evaluation, adaptive choice of subheuristics)
• Implementation in an executable software package (e.g., Python)
• Design of appropriate pre-tests for heuristic fine-tuning (e.g., diversification vs. intensification)
• Application to business cases: Generation of practical test instances and design of sutiable experiments to evaluate performance and to investigate managerial implications
   

every summer term since 2022