Learning Outcomes
The implementation of numerical optimization in mechanical, manufacturing and process systems. Major emphasis is given in the optimization problem formulation using a single or multiple criteria using gradient based methods and non-gradient probabilistic methods.
Course Content (Syllabus)
Optimization problem formulation
Decision hierarchy, selection of criteria, decision variables (continuous, discrete), mathematical model formulation, constraints, parameters
Applications (1st Assignment):
Manufacturing: Mechanical system model development
Energy: Thermal process model development.
Industrial management: Supply chain modellig.
Numerical Optimization (gradient-based)
Unconstrained and Constrained problems
Linear and non-linear programming
Linear and non-linear integer programming
Solution of optimality conditions, Optimal solution sensitivity
Applications (2nd Assignment) – Continuous decision variables (3rd Assignment) – Continuous and discrete decision variables
Manufacturing: Mechanical system optimization.
Energy: Heat exchanger network optimization.
Industrial management: Supply chain optimization.
Optimization using probabilistic methods (non-gradient methods)
Simulated annealing, genetic algorithms.
Applications (4th Assignment) – Implementation of probabilistic optimization methods
Manufacturing: Mechanical system optimization.
Energy: Heat exchanger network optimization.
Industrial management: Supply chain optimization.
Multi-objective optimization
Pareto front. Numerical optimization of multi-objective optimization problems.
Applications (5th Assignment) – Implementation of multi-objective optimization methods
Manufacturing: Mechanical system optimization.
Energy: Heat exchanger network optimization.
Industrial management: Supply chain optimization.
Optimization under uncertainty
Uncertainty characterization – Problem formulation and solution
Applications (6th Assignment) – Implementation of optimization methods under uncertainty.
Manufacturing: Mechanical system optimization.
Energy: Heat exchanger network optimization.
Industrial management: Supply chain optimization.
Optimization of dynamic problems
Time discretization. Decision vector parameterization. Numerical solution (direct methods, sequential method, multiple shooting)
Applications (4th Assignment) – Implementation of dynamic optimization methods.
Manufacturing: Mechanical system optimization.
Energy: Heat exchanger network optimization.
Industrial management: Supply chain optimization.
Keywords
optimization, nonlinear programming, mixed integer programming, multi-objective optimization
Course Bibliography (Eudoxus)
Βελτιστοποίηση και Δυναμική Προσομοίωση Διεργασιών, Ιωάννης Κούκος, Εκδόσεις Τζιόλα.
Βελτιστοποίηση και Λογισμικό Κατασκευών: Πεπερασμένα Στοιχεία, Ισογεωμετρικά Στοιχεία, Συνοριακά Στοιχεία, Χριστόφορος Προβατίδης, Εκδόσεις Τζιόλα.
Σχεδιασμός Θερμικών Διεργασιών, Μ. Κροκίδα, Δ. Μαρίνος - Κουρής, Ζ. Μαρούλης
Σχεδιασμός και Οικονομική Μελέτη Εγκαταστάσεων για Μηχανικούς, Peters Max, Timmerhaus Klaus D., West Ronald E, Εκδόσεις ΤΖΙΟΛΑ
Εισαγωγή στο Σχεδιασμό Χημικών Εργοστασίων , Κούκος Ι. , Εκδόσεις ΤΖΙΟΛΑ
Additional bibliography for study
Ενεργειακός Τομέας
Ajah Α.Ν., J. Grievink, P.L.J. Swinkels, “Delft Design Matrix: Framework for conceptual process design of future plants”, 2003.
Biegler L.T., I.E. Grossmann, A.W. Westerberg, “Systematic methods of chemical process design”, Prentice-Hall, 1997.
Douglas J.M., “Conceptual design of processes”, McGraw-Hill, 1988.
Jaluria Y., “Design and optimization of thermal systems”, CRC Press, 2008.
Seider W.D., J.D. Seader, D.R. Lewin, “Product and process design principles”, Wiley, 2nd Ed., 2004.
Smith R., “Chemical Process – Design and Integration”, Wiley, 2005.
Suryanarayana N.V., Oner Arici, and N. Suryanarayana, “Design and Simulation of Thermal Systems”, McGraw Hill,