Addressing the Weaknesses of Multi-Criteria Decision-Making Methods using Python - Erpolat Tasabat, Semra;KIRAL ÖZKAN, Tugba;Aydin, Olgun
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The book aims to draw attention to the weaknesses in Multi-Criteria Decision-Making (MCDM) methods and provide insights to improve the decision-making process. By addressing these weaknesses, it seeks to enhance the accuracy and effectiveness of MCDM methods in selecting the best alternatives in various fields. The book covers popular MCDM methods such as TOPSIS, ELECTRE, VIKOR, and PROMETHEE. It compares traditional methods with the proposed modified Human Development Index (HDI) data using Python code examples. The target audience for the book includes computer scientists, engineers,…mehr

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Produktbeschreibung
The book aims to draw attention to the weaknesses in Multi-Criteria Decision-Making (MCDM) methods and provide insights to improve the decision-making process. By addressing these weaknesses, it seeks to enhance the accuracy and effectiveness of MCDM methods in selecting the best alternatives in various fields. The book covers popular MCDM methods such as TOPSIS, ELECTRE, VIKOR, and PROMETHEE. It compares traditional methods with the proposed modified Human Development Index (HDI) data using Python code examples. The target audience for the book includes computer scientists, engineers, business, and financial management professionals, as well as anyone interested in MCDM and its applications.
Autorenporträt
Semra Erpolat Tasabat completed her education in Statistics, earning her Ph.D. from Mimar Sinan Fine Arts University and Marmara University. She has been a full-time lecturer at Mimar Sinan Fine Arts University. Dr. Tas abat has made significant contributions to the field of statistics and decision methods, and her expertise is evident through her academic appointments and research activities.

Tug ba Kiral Ozkan is a full-time lecturer at Bahces ehir University. She received her Ph.D. in Operations Research from Marmara University, Institute of Social Sciences. Her research interests include measurement and evaluation, optimization methods, and multi-criteria decision-making. She has published scientific journals and conference papers on optimization, multi-criteria decision-making, social network analysis, statistical data analysis, and machine learning. She offers research methods and statistical data analysis courses in undergraduate and graduate programs atBAU.

Olgun Aydin holds a Ph.D. and is an expert in the field of deep learning, statistics, and machine learning. He works as an Assistant Professor at Gdansk University of Technology in Poland. Dr. Aydin is the author and co-author of several R packages. He is passionate about sharing his expertise in data science and is actively involved in the Why R? Foundation and the Polish Artificial Intelligence Society.