This course introduces heuristic and metaheuristic optimization methods for solving complex optimization problems that are difficult to solve exactly within reasonable computational time. The course covers constructive heuristics, local search, neighborhood design, improvement heuristics, and major metaheuristic frameworks such as simulated annealing, tabu search, genetic algorithms, variable neighborhood search, and ant colony optimization. Emphasis is placed on algorithm design principles, intensification and diversification mechanisms, parameter tuning, and computational evaluation. Students will gain both theoretical understanding and practical experience through implementation and testing on representative industrial engineering problems such as routing, scheduling, assignment, and network design.

The objective of this course is to introduce students to the principles, methods, and applications of heuristic optimization through a range of combinatorial optimization problems relevant to modern Industrial Engineering. Upon completing this course, students will be able to:
- Explain the role of heuristic and metaheuristic optimization methods in solving complex combinatorial optimization problems,
- Design, implement, and analyze constructive heuristics, local search methods, and improvement procedures,
- Develop and apply major metaheuristics such as simulated annealing, tabu search, genetic algorithms, variable neighborhood search, and ant colony optimization.
- Evaluate heuristic algorithms through computational experiments using appropriate performance metrics, benchmarking, and sensitivity analysis,
- Compare exact and heuristic approaches in terms of solution quality, computational effort, scalability, and practical applicability,
- Develop complete heuristic solution approaches for practical optimization problems, including parameter tuning and computational performance analysis.

Textbook
• Éric D. Taillard, Design of Heuristic Algorithms for Hard Optimization. Graduate Texts in Operations Research. Springer, 2022.

Recommended Reading
•Talbi, E.-G., Metaheuristics: From Design to Implementation. Wiley, 2009.
•Gendreau, M. and Potvin, J.-Y. (eds.). Handbook of Metaheuristics. 3rd ed. Springer, 2019.