Multi-Criteria Methods of Optimization of Decision-Making Processes
The aim of the course is to provide participants with knowledge of the use of multi-criteria optimization methods in decision-making processes.
Opis kursu
The course was created as part of the project "MOOC @ PB-Modern technologies in the education process", contract no. POWR.03.01.00-00-W040 / 18-00.
COURSE DESCRIPTION
Course participants will learn about the fields of application of multi-criteria optimization methods, detailed calculation procedures of the methods SAW, TOPSIS, AHP, application of fuzzy sets in multi-criteria analysis. They will acquire the ability to implement methods in a spreadsheet (MS Excel) and to interpret the results obtained using them.
The course is dedicated to 1st and 2nd degree students of Logistics, Management as well as Engineering Management as a supplement to the study program. Additionally, it can be used by people who want to broaden their knowledge of decision optimization methods.
The course consists of 6 modules plus an introduction and summary, which are carried out at the participants' own pace. The content of the course is presented in the form of a lecture, multimedia exercises, and case study analysis. Spreadsheet files with solution examples are also included.
ENTRY REQUIREMENTS
To start the course, it is advisable to have basic knowledge of operations research and spreadsheet use (e.g., MS Excel).
COURSE OBJECTIVES
- You will acquire theoretical knowledge about selected methods of multi-criteria optimization of decision problems (SAW, TOPSIS, AHP).
- You will learn about calculation algorithms and possible fields of application of the discussed methods.
- You will learn how to apply the discussed methods in practice.
- You will learn to analyze sample problems with the use of spreadsheets.
SUBJECT MATTERS
INTRODUCTION
The Introduction presents the description of the course, the course content and the course completion requirements, and contact with the author of the course.
MODULE 1 - Basics of decision optimization
The first module discusses the multi-criteria methods and their division.
MODULE 2 - SAW method algorithm
The second module presents the assumptions, calculation procedures and examples of solutions using the SAW method.
MODULE 3 - TOPSIS method algorithm
The third module presents the assumptions, calculation procedures and examples of solutions using the TOPSIS method.
MODULE 4 - AHP method algorithm
The fourth module presents the assumptions, calculation procedures and examples of solutions using the AHP method.
MODULE 5 - Application of fuzzy sets in multi-criteria analysis
The fifth module presents the assumptions, calculation procedures and examples of the use of fuzzy numbers in the discussed methods.
MODULE 6 - Assessment of the usefulness of the discussed methods and interpretation of the obtained results
The sixth module presents the a summary of the course content.
SUMMARY
The final part presents the rules for completing the course and obtaining a certificate of completion.
COURSE COMPLETION REQUIREMENTS
To pass the course, the test questions at the end of each module must be answered. The condition for completing the course is obtaining at least 50% of the total points. The exercises are closed, single-choice questions. They concern the theoretical part and the presented practical solutions.
COURSE CERTIFICATE REQUIREMENTS
Participants who achieve a score of at least 50% points of all tests will receive a course completion certificate.
COURSE INSTRUCTOR
dr inż. Wojciech Zalewski
Assistant professor, Białystok University of Technology, Faculty of Management Engineering. The area of scientific interest focuses on the use of mathematical methods, information technologies and artificial intelligence tools in decision-making processes and additionally on the estimation of operating states of distribution networks. Focuses particularly on optimization methods. Teaching activity covers a wide range of issues related to the use of information technologies in management processes and methods of decision-making processes optimization.