TOPSIS and its Extensions: A Distance-Based MCDM Approach

The book aims to demonstrate the development of TOPSIS and serve as a handbook for graduate students, researchers, analysts, and professionals interested in TOPSIS and MCDM methods. It provides step-by-step illustrations and covers basic process, variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases.

Format: Hardback
Length: 221 pages
Publication date: 08 September 2022
Publisher: Springer International Publishing AG

The primary goal of this book is to offer a comprehensive collection of resources to showcase the evolution of the TOPSIS method and serve as a valuable handbook. It encompasses the fundamental steps of TOPSIS, various alternative processes, detailed explanations of properties, theoretical advancements, and illustrative examples accompanied by real-world case studies. This text is intended for a diverse audience, including graduate students, researchers, analysts, and professionals with an interest in TOPSIS, a distance-based algorithm, and who seek to compare it with other MCDM methods. Additionally, the book serves as a valuable research reference and a self-learning tool, featuring step-by-step illustrations that cater to the MCDM community.
The book aims to provide a comprehensive guide to the development of the TOPSIS method, encompassing its fundamental principles, various approaches, and practical applications. It serves as a valuable resource for graduate students, researchers, analysts, and professionals interested in TOPSIS, a distance-based algorithm, and its comparison with other MCDM methods. The book offers detailed explanations of TOPSIS processes, including the selection of criteria, weighting, and interpretation of results. It also includes numerous variant processes and property explanations to enhance the understanding and application of the method. Furthermore, the book presents theoretical developments and illustrative examples with real-world cases to demonstrate the practical significance and effectiveness of TOPSIS in decision-making scenarios. By presenting a self-learning approach with step-by-step illustrations, the book facilitates the understanding and application of TOPSIS by the MCDM community.

The primary goal of this book is to offer a comprehensive collection of resources to showcase the evolution of the TOPSIS method and serve as a valuable handbook. It encompasses the fundamental steps of TOPSIS, various alternative processes, detailed explanations of properties, theoretical advancements, and illustrative examples accompanied by real-world case studies. This text is intended for a diverse audience, including graduate students, researchers, analysts, and professionals with an interest in TOPSIS, a distance-based algorithm, and who seek to compare it with other MCDM methods. Additionally, the book serves as a valuable research reference and a self-learning tool, featuring step-by-step illustrations that cater to the MCDM community.

The book aims to provide a comprehensive guide to the development of the TOPSIS method, encompassing its fundamental principles, various approaches, and practical applications. It serves as a valuable resource for graduate students, researchers, analysts, and professionals interested in TOPSIS, a distance-based algorithm, and its comparison with other MCDM methods. The book offers detailed explanations of TOPSIS processes, including the selection of criteria, weighting, and interpretation of results. It also includes numerous variant processes and property explanations to enhance the understanding and application of the method. Furthermore, the book presents theoretical developments and illustrative examples with real-world cases to demonstrate the practical significance and effectiveness of TOPSIS in decision-making scenarios. By presenting a self-learning approach with step-by-step illustrations, the book facilitates the understanding and application of TOPSIS by the MCDM community.

Weight: 530g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031095764
Edition number: 1st ed. 2022