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Vorlesung und Übungen

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