Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)

Sylabus przedmiotu FUNDAMENTALS OF OPTIMIZATION TECHNIQUES IN ENGINEERING:

Informacje podstawowe

Kierunek studiów Wymiana międzynarodowa
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta
Obszary studiów —
Profil
Moduł —
Przedmiot FUNDAMENTALS OF OPTIMIZATION TECHNIQUES IN ENGINEERING
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Inżynierii Chemicznej i Procesowej
Nauczyciel odpowiedzialny Halina Murasiewicz <Halina.Murasiewicz@zut.edu.pl>
Inni nauczyciele Bogdan Ambrożek <Bogdan.Ambrozek@zut.edu.pl>
ECTS (planowane) 4,0 ECTS (formy) 4,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny — Grupa obieralna —

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
ćwiczenia audytoryjneA1 30 2,00,50zaliczenie
wykładyW1 30 2,00,50zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Chemical engineering fundamentals
W-2Applied Mathematics

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Student after successful completion of course is excepted to have: • Knowledge of basic optimization techniques. • Ability to formulate decision problems as optimization problems. • Ability to solve simple problems, select appropriate method and to use the right software to solve complicated problems.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
ćwiczenia audytoryjne
T-A-1Students will solve individual problems during the classes. The typical probles are similar to these: Golden-section search5
T-A-2Newton's Method. Grid search method5
T-A-3Relaxation (approximation)5
T-A-4Gradients method5
T-A-5Lagrange multiplier methods5
T-A-6Simplex Method5
30
wykłady
T-W-1Introduction to the optimization problems. Fundamental definitions: goal function, optimization variables, requirements and conditions of the unique optimal solution2
T-W-2Fundamentals of mathematical modelling2
T-W-3Fundamentals of the non-gradient optimization methods2
T-W-4Unconstrained Optimization - introduction, definition and examples2
T-W-5Direct search methods: golden section and Fibonacci techniques, Newton’s method2
T-W-6Discrete Optimization problems2
T-W-7Introduction to genetic algorithms2
T-W-8Optimization methods for constrained optimization problems: Lagrange and penalty function methods4
T-W-9Optimization methods: Pareto compromise approach.2
T-W-10Fundamentals of the linear programming: graphic method.2
T-W-11Simplex method for linear optimization problems.2
T-W-12Fundamentals of dynamic optimization2
T-W-13Fundamentals of robust optimization:Local robustness, Global robustness, Stability radius4
30

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
ćwiczenia audytoryjne
A-A-1Class participation30
A-A-2One-on-One Teaching Consultations15
A-A-3Self-preparation of reports5
A-A-4Self-preparation for classes10
60
wykłady
A-W-1Class participation30
A-W-2One-on-One Teaching Consultations15
A-W-3Self-preparation for lectures10
A-W-4Self-preparation of reports5
60

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1activating methods: lecture and didactic discussion
M-2practical methods - tutorials

Sposoby oceny

KODSposób oceny
S-1Ocena formująca: assessment of progress of the work - monthly
S-2Ocena podsumowująca: written final report/test

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WTiICh_1-_null_W01
Student has theoretical knowledge of the theory and methods of optimization which allows the analysis and modeling of data and processes		——C-1T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-8, T-W-9, T-W-10, T-W-11, T-W-12, T-W-13M-1S-1, S-2

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WTiICh_1-_null_U01
Student knows chosen methods and software tools for deterministic and non-deterministic optimization and knows how to use them in solving optimization problems in engineering field. Student knows how to formulate optimization problems and how to select a proper optimization method.		——C-1T-A-1, T-A-2, T-A-3, T-A-4, T-A-5, T-A-6M-2S-1

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WTiICh_1-_null_K01
Student can solve simple task independently or in a group		——C-1T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-8, T-W-9, T-W-10, T-W-11, T-W-12, T-W-13, T-A-1, T-A-2, T-A-3, T-A-4, T-A-5, T-A-6M-1, M-2S-1, S-2

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
WM-WTiICh_1-_null_W01
Student has theoretical knowledge of the theory and methods of optimization which allows the analysis and modeling of data and processes		2,0
3,0Student is able to list and explain methods used in optimization techniques
3,5
4,0
4,5
5,0

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
WM-WTiICh_1-_null_U01
Student knows chosen methods and software tools for deterministic and non-deterministic optimization and knows how to use them in solving optimization problems in engineering field. Student knows how to formulate optimization problems and how to select a proper optimization method.		2,0
3,0Student is able to select appropriate methods for calculation problems
3,5
4,0
4,5
5,0

Kryterium oceny - inne kompetencje społeczne i personalne

Efekt uczenia sięOcenaKryterium oceny
WM-WTiICh_1-_null_K01
Student can solve simple task independently or in a group		2,0
3,0Student is able to understand basic level the needs of the continuous training and development in the field of optimization techniques
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. Belegundu A. and T. Chandrupatla, Optimization Concepts and Applications in Engineering, Prentice Hall, 1999
  2. Gen, M. and R. Cheng, Genetic Algorithms and Engineering Optimization, Wiley, 2000
  3. Edgar, T.F., Himmelblau, D.M., L.S. Lasdon, Optimization of Chemical Processes, McGraw Hill, 2011

Literatura dodatkowa

  1. Fletcher R., Practical Methods of Optimization, John Wiley, 1980
  2. Luenberger, David G., Ye, Yinyu, Linear and Nonlinear Programming, Springer, 2008

Treści programowe - ćwiczenia audytoryjne

KODTreść programowaGodziny
T-A-1Students will solve individual problems during the classes. The typical probles are similar to these: Golden-section search5
T-A-2Newton's Method. Grid search method5
T-A-3Relaxation (approximation)5
T-A-4Gradients method5
T-A-5Lagrange multiplier methods5
T-A-6Simplex Method5
30

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Introduction to the optimization problems. Fundamental definitions: goal function, optimization variables, requirements and conditions of the unique optimal solution2
T-W-2Fundamentals of mathematical modelling2
T-W-3Fundamentals of the non-gradient optimization methods2
T-W-4Unconstrained Optimization - introduction, definition and examples2
T-W-5Direct search methods: golden section and Fibonacci techniques, Newton’s method2
T-W-6Discrete Optimization problems2
T-W-7Introduction to genetic algorithms2
T-W-8Optimization methods for constrained optimization problems: Lagrange and penalty function methods4
T-W-9Optimization methods: Pareto compromise approach.2
T-W-10Fundamentals of the linear programming: graphic method.2
T-W-11Simplex method for linear optimization problems.2
T-W-12Fundamentals of dynamic optimization2
T-W-13Fundamentals of robust optimization:Local robustness, Global robustness, Stability radius4
30

Formy aktywności - ćwiczenia audytoryjne

KODForma aktywnościGodziny
A-A-1Class participation30
A-A-2One-on-One Teaching Consultations15
A-A-3Self-preparation of reports5
A-A-4Self-preparation for classes10
60
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Class participation30
A-W-2One-on-One Teaching Consultations15
A-W-3Self-preparation for lectures10
A-W-4Self-preparation of reports5
60
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WTiICh_1-_null_W01Student has theoretical knowledge of the theory and methods of optimization which allows the analysis and modeling of data and processes
Cel przedmiotuC-1Student after successful completion of course is excepted to have: • Knowledge of basic optimization techniques. • Ability to formulate decision problems as optimization problems. • Ability to solve simple problems, select appropriate method and to use the right software to solve complicated problems.
Treści programoweT-W-1Introduction to the optimization problems. Fundamental definitions: goal function, optimization variables, requirements and conditions of the unique optimal solution
T-W-2Fundamentals of mathematical modelling
T-W-3Fundamentals of the non-gradient optimization methods
T-W-4Unconstrained Optimization - introduction, definition and examples
T-W-5Direct search methods: golden section and Fibonacci techniques, Newton’s method
T-W-6Discrete Optimization problems
T-W-7Introduction to genetic algorithms
T-W-8Optimization methods for constrained optimization problems: Lagrange and penalty function methods
T-W-9Optimization methods: Pareto compromise approach.
T-W-10Fundamentals of the linear programming: graphic method.
T-W-11Simplex method for linear optimization problems.
T-W-12Fundamentals of dynamic optimization
T-W-13Fundamentals of robust optimization:Local robustness, Global robustness, Stability radius
Metody nauczaniaM-1activating methods: lecture and didactic discussion
Sposób ocenyS-1Ocena formująca: assessment of progress of the work - monthly
S-2Ocena podsumowująca: written final report/test
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student is able to list and explain methods used in optimization techniques
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WTiICh_1-_null_U01Student knows chosen methods and software tools for deterministic and non-deterministic optimization and knows how to use them in solving optimization problems in engineering field. Student knows how to formulate optimization problems and how to select a proper optimization method.
Cel przedmiotuC-1Student after successful completion of course is excepted to have: • Knowledge of basic optimization techniques. • Ability to formulate decision problems as optimization problems. • Ability to solve simple problems, select appropriate method and to use the right software to solve complicated problems.
Treści programoweT-A-1Students will solve individual problems during the classes. The typical probles are similar to these: Golden-section search
T-A-2Newton's Method. Grid search method
T-A-3Relaxation (approximation)
T-A-4Gradients method
T-A-5Lagrange multiplier methods
T-A-6Simplex Method
Metody nauczaniaM-2practical methods - tutorials
Sposób ocenyS-1Ocena formująca: assessment of progress of the work - monthly
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student is able to select appropriate methods for calculation problems
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WTiICh_1-_null_K01Student can solve simple task independently or in a group
Cel przedmiotuC-1Student after successful completion of course is excepted to have: • Knowledge of basic optimization techniques. • Ability to formulate decision problems as optimization problems. • Ability to solve simple problems, select appropriate method and to use the right software to solve complicated problems.
Treści programoweT-W-1Introduction to the optimization problems. Fundamental definitions: goal function, optimization variables, requirements and conditions of the unique optimal solution
T-W-2Fundamentals of mathematical modelling
T-W-3Fundamentals of the non-gradient optimization methods
T-W-4Unconstrained Optimization - introduction, definition and examples
T-W-5Direct search methods: golden section and Fibonacci techniques, Newton’s method
T-W-6Discrete Optimization problems
T-W-7Introduction to genetic algorithms
T-W-8Optimization methods for constrained optimization problems: Lagrange and penalty function methods
T-W-9Optimization methods: Pareto compromise approach.
T-W-10Fundamentals of the linear programming: graphic method.
T-W-11Simplex method for linear optimization problems.
T-W-12Fundamentals of dynamic optimization
T-W-13Fundamentals of robust optimization:Local robustness, Global robustness, Stability radius
T-A-1Students will solve individual problems during the classes. The typical probles are similar to these: Golden-section search
T-A-2Newton's Method. Grid search method
T-A-3Relaxation (approximation)
T-A-4Gradients method
T-A-5Lagrange multiplier methods
T-A-6Simplex Method
Metody nauczaniaM-1activating methods: lecture and didactic discussion
M-2practical methods - tutorials
Sposób ocenyS-1Ocena formująca: assessment of progress of the work - monthly
S-2Ocena podsumowująca: written final report/test
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Student is able to understand basic level the needs of the continuous training and development in the field of optimization techniques
3,5
4,0
4,5
5,0