Multiple criteria decision aid : methods, examples and Python implementations / Jason Papathanasiou, Nikolaos Ploskas.
Papathanasiou, Jason, author.; Ploskas, Nikolaos, author.
2018
QA76
Available Online

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Title Multiple criteria decision aid : methods, examples and Python implementations / Jason Papathanasiou, Nikolaos Ploskas.
Author Papathanasiou, Jason, author.
ISBN 9783319916484 (electronic book)
3319916483 (electronic book)
9783319916460
3319916467
DOI https://doi.org/10.1007/978-3-319-91648-4
Published Cham, Switzerland : Springer, [2018]
Copyright ©2018
Language English
Description 1 online resource.
Item Number 10.1007/978-3-319-91648-4 doi
Call Number QA76
Dewey Decimal Classification 004
Summary Multiple criteria decision aid (MCDA) methods are illustrated in this book through theoretical and computational techniques utilizing Python. Existing methods are presented in detail with a step by step learning approach. Theoretical background is given for TOPSIS, VIKOR, PROMETHEE, SIR, AHP, goal programming, and their variations. Comprehensive numerical examples are also discussed for each method in conjunction with easy to follow Python code. Extensions to multiple criteria decision making algorithms such as fuzzy number theory and group decision making are introduced and implemented through Python as well. Readers will learn how to implement and use each method based on the problem, the available data, the stakeholders involved, and the various requirements needed. Focusing on the practical aspects of the multiple criteria decision making methodologies, this book is designed for researchers, practitioners and advanced graduate students in the applied mathematics, information systems, operations research and business administration disciplines, as well as other engineers and scientists oriented in interdisciplinary research. Readers will greatly benefit from this book by learning and applying various MCDM/A methods. (Adiel Teixeira de Almeida, CDSID-Center for Decision System and Information Development, Universidade Federal de Pernambuco, Recife, Brazil) Promoting the development and application of multicriteria decision aid is essential to ensure more ethical and sustainable decisions. This book is a great contribution to this objective. It is a perfect blend of theory and practice, providing potential users and researchers with the theoretical bases of some of the best-known methods as well as with the computing tools needed to practice, to compare and to put these methods to use. (Jean-Pierre Brans, Vrije Universiteit Brussel, Brussels, Belgium) This book is intended for researchers, practitioners and students alike in decision support who wish to familiarize themselves quickly and efficiently with multicriteria decision aiding algorithms. The proposed approach is original, as it presents a selection of methods from the theory to the practical implementation in Python, including a detailed example. This will certainly facilitate the learning of these techniques, and contribute to their effective dissemination in applications. (Patrick Meyer, IMT Atlantique, Lab-STICC, Univ. Bretagne Loire, Brest, France).-- Provided by publisher.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Vendor-supplied metadata.
Added Author Ploskas, Nikolaos, author.
Series Springer optimization and its applications ; v. 136.
Available in Other Form Multiple criteria decision aid.
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Record Appears in Online Resources > Ebooks
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Intro; Foreword; Preface; Contents; 1 TOPSIS; 1.1 Introduction; 1.2 Methodology; 1.2.1 Numerical Example; 1.2.2 Python Implementation; 1.2.3 Rank Reversal; 1.3 Fuzzy TOPSIS for Group Decision Making; 1.3.1 Preliminaries: Elements of Fuzzy Numbers Theory; 1.3.2 Fuzzy TOPSIS Methodology; 1.3.3 Numerical Example; 1.3.4 Python Implementation; References; 2 VIKOR; 2.1 Introduction; 2.2 Methodology; 2.2.1 Numerical Example; 2.2.2 Python Implementation; 2.3 Fuzzy VIKOR for Group Decision Making; 2.3.1 Preliminaries: Trapezoidal Fuzzy Numbers; 2.3.2 Fuzzy VIKOR Methodology; 2.3.3 Numerical Example.

2.3.4 Python ImplementationReferences; 3 PROMETHEE; 3.1 Introduction; 3.2 Methodology; 3.2.1 Numerical Example; 3.2.2 Python Implementation; 3.2.3 Visual Decision Aid: The GAIA Method; 3.3 PROMETHEE Group Decision Support System; 3.4 PROMETHEE V; 3.4.1 Numerical Example; 3.4.2 Python Implementation; References; 4 SIR; 4.1 Introduction; 4.2 Methodology; 4.2.1 Numerical Example; 4.2.2 Python Implementation; References; 5 AHP; 5.1 Introduction; 5.2 Methodology; 5.2.1 Numerical Example; 5.2.2 Python Implementation; 5.2.3 Rank Reversal; References; 6 Goal Programming; 6.1 Introduction.

6.2 Classical Goal Programming6.2.1 Numerical Example; 6.2.2 Python Implementation; 6.3 Weighted Goal Programming; 6.3.1 Numerical Example; 6.3.2 Python Implementation; 6.4 Lexicographic Goal Programming; 6.4.1 Numerical Example; 6.4.2 Python Implementation; 6.5 Chebyshev Goal Programming; 6.5.1 Numerical Example; 6.5.2 Python Implementation; References; Appendix: Revised Simos; Appendix: Revised Simos; A.1 Introduction; A.2 Methodology; A.2.1 Numerical Example; A.2.2 Python Implementation; References; Index.

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