Upon successful completion of the course the student will be able to:

• Recall optimization methodologies, linear, non-linear, dynamic programming and advanced optimization methodologies.
• Recognize the mathematical expression of optimization for an environmental problem and which methodology is most suitable for solving any kind of problem.
• Select the appropriate solution method and determine the optimal solution
• Αpply the taught methodologies to environmental problems.

• Work Autonomously
• Decision making
• Project planning and management

1. INTRODUCTION TO THE THEORY OF OPTIMIZATION: Introduction.
2. Optimization Model Classification, Nonlinear Optimization, Hollow Sets and Functions.
3. Mathematical Optimization Theorems.
4. Geometry of the Mathematical Optimization Problem.
5. CLASSIC OPTIMIZATION: Unlimited Optimization Problems, Lagrange Multipliers.
6. LINEAR PROGRAMMING: Optimization in Linear Programming Problems
7. LINEAR PROGRAMMING: Simplex Method.
8. NON-LINEAR PROGRAMMING: Introduction, Unlimited Optimization Methods,
9. NON-LINEAR PROGRAMMING Optimization Methods with Constraints, Dynamic Programming.
10. DYNAMIC PROGRAMMING: Introduction and basic concepts.
11. ADVANCED METHODS OF OPTIMIZATION: Genetic algorithms,
12. ADVANCED METHODS OF OPTIMIZATION: Fuzzy Logic, Neural Networks.
13. Matlab applications.

Use of Information and Communication Technology

Specialized software; E-class support

Assessment Method

Ι. Written final examination (80%).
- Theoretical problems with data to be resolved.

ΙΙ. Project 20(%).

• Μέθοδοι Βελτιστοποίησης Περιβαλλοντικών Συστημάτων, Καρατζάς Γεώργιος, Παπαδοπούλου Μαρία
• Στοιχεία Βελτιστοποίησης, Ευστράτιος Ε. Τζιρτζιλάκης