Module also offered within study programmes:
General information:
Name:
Multi-criteria optimization models for real life applications
Course of study:
2019/2020
Code:
ZZIP-2-307-n
Faculty of:
Management
Study level:
Second-cycle studies
Specialty:
-
Field of study:
Management and Production Engineering
Semester:
3
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Part-time studies
Course homepage:
 
Responsible teacher:
dr inż. Sawik Bartosz (BSawik@zarz.agh.edu.pl)
Module summary

This course includes practical examples of multi-criteria optimization problems.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 Student is able to acquire knowledge by oneself. ZIP2A_K01 Project
Skills: he can
M_U001 Student is able to identify the type of a multi-criteria optimization models, to choose and implement appropriate optimisation model for selected real life applications. ZIP2A_U01 Execution of exercises,
Project,
Test
M_U002 Student is able to model and solve standard multicriteria problems in the area of decision science. ZIP2A_U02 Execution of exercises,
Project,
Test
Knowledge: he knows and understands
M_W001 Student knows Mathematical Programming modelling techniques, as well as standard multicriterial combinatorial models applied in decision science. ZIP2A_W02 Execution of exercises,
Project,
Test
M_W002 Student knows multi-criteria combinatorial optimisation methods and tools applied in decision science. ZIP2A_W02 Execution of a project,
Test
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
28 14 0 0 0 0 0 0 0 14 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 Student is able to acquire knowledge by oneself. - - - - - - - - + - -
Skills
M_U001 Student is able to identify the type of a multi-criteria optimization models, to choose and implement appropriate optimisation model for selected real life applications. - - - - - - - - + - -
M_U002 Student is able to model and solve standard multicriteria problems in the area of decision science. - - - - - - - - + - -
Knowledge
M_W001 Student knows Mathematical Programming modelling techniques, as well as standard multicriterial combinatorial models applied in decision science. + - - - - - - - - - -
M_W002 Student knows multi-criteria combinatorial optimisation methods and tools applied in decision science. + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 125 h
Module ECTS credits 5 ECTS
Udział w zajęciach dydaktycznych/praktyka 28 h
Preparation for classes 42 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 20 h
Realization of independently performed tasks 35 h
Module content
Lectures (14h):

This course will consist of three parts. All parts includes practical examples of multi-criteria optimization problems, such as: portfolio optimization for investments, assignment problems for hospital and Green Vehicle Routing problems for supermarket transportation chain.
The first part includes revision of multi-criteria combinatorial, linear and mixed integer programming models.
In the second part selected multi-criteria models of combinatorial optimization and mixed integer programming are going to be explained.
The last part comprises definition of multi-objective optimization with explanation of weighted-sum, lexicographic and reference point approaches.

Workshops (14h):

This course will consist of three parts.
All parts includes practical examples of multi-criteria optimization problems, such as: portfolio optimization for investments, assignment problems for hospital and Green Vehicle Routing problems for supermarket transportation chain.
The first part includes revision of multi-criteria combinatorial, linear and mixed integer programming models.
In the second part selected multi-criteria models of combinatorial optimization and mixed integer programming are going to be explained.
The last part comprises definition of multi-objective optimization with explanation of weighted-sum, lexicographic and reference point approaches.

Additional information
Teaching methods and techniques:
  • Lectures: Treści prezentowane na wykładzie są przekazywane w formie prezentacji multimedialnej w połączeniu z klasycznym wykładem tablicowym wzbogaconymi o pokazy odnoszące się do prezentowanych zagadnień.
  • Workshops: Podczas zajęć audytoryjnych studenci na tablicy rozwiązują zadane wcześniej problemy. Prowadzący na bieżąco dokonuje stosowanych wyjaśnień i moderuje dyskusję z grupą nad danym problemem.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

project

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego.
  • Workshops:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Studenci przystępując do ćwiczeń są zobowiązani do przygotowania się w zakresie wskazanym każdorazowo przez prowadzącego (np. w formie zestawów zadań). Ocena pracy studenta może bazować na wypowiedziach ustnych lub pisemnych w formie kolokwium, co zgodnie z regulaminem studiów AGH przekłada się na ocenę końcową z tej formy zajęć.
Method of calculating the final grade:

Exercises 20%, project (case study) 30%, written tests 50%.

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

According to RS

Prerequisites and additional requirements:

Prerequisites and additional requirements not specified

Recommended literature and teaching resources:

SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). A Multicriteria Analysis for the Green VRP: A Case
Discussion for the Distribution Problem of a Spanish Retailer, Transportation Research Procedia.
SAWIK B. (2016). Triple-Objective Models for Portfolio Optimisation with Symmetric and Percentile Risk
Measures, International Journal of Logistics Systems and Management, Vol. 25(1): 96-107
SAWIK B. (2013). A Single and Triple-Objective Mathematical Programming Models for Assignment of
Services in a Health Care Institution, International Journal of Logistics Systems and Management, Vol. 15(2/3): 249–259
SAWIK B. (2013). A Review of Multi-Criteria Portfolio Optimization by Mathematical Programming, chapter in: Dash G.H., Thomaidis N. (Eds.) Recent Advances in Computational Finance, Nova Science Publishers, New York, USA, pp. 149-172
NEMHAUSER G.L., WOLSEY L.A. (1999). Integer and Combinatorial Optimization. John Wiley & Sons,
Toronto, Canada.
STEUER R.E. (1986). Multiple Criteria Optimization: Theory, Computation and Application. John Wiley &
Sons, New York, USA.

Scientific publications of module course instructors related to the topic of the module:

SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). A Multicriteria Analysis for the Green VRP: A Case
Discussion for the Distribution Problem of a Spanish Retailer, Transportation Research Procedia.
SAWIK B. (2016). Triple-Objective Models for Portfolio Optimisation with Symmetric and Percentile Risk
Measures, International Journal of Logistics Systems and Management, Vol. 25(1): 96-107
SAWIK B. (2013). A Single and Triple-Objective Mathematical Programming Models for Assignment of
Services in a Health Care Institution, International Journal of Logistics Systems and Management, Vol. 15(2/3): 249–259
SAWIK B. (2013). A Review of Multi-Criteria Portfolio Optimization by Mathematical Programming, chapter in: Dash G.H., Thomaidis N. (Eds.) Recent Advances in Computational Finance, Nova Science Publishers, New York, USA, pp. 149-172
NEMHAUSER G.L., WOLSEY L.A. (1999). Integer and Combinatorial Optimization. John Wiley & Sons,
Toronto, Canada.
STEUER R.E. (1986). Multiple Criteria Optimization: Theory, Computation and Application. John Wiley &
Sons, New York, USA.

Additional information:

None