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Multiple Criteria Decision Aid

Methods, Examples and Python Implementations

  • Book
  • © 2018

Overview

Authors:
  1. Jason Papathanasiou

    1. Department of Business Administration, University of Macedonia, Thessaloniki, Greece

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    You can also search for this author in PubMed   Google Scholar

  2. Nikolaos Ploskas

    1. Carnegie Mellon University, Pittsburgh, USA

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    You can also search for this author in PubMed   Google Scholar

  • Brings readers to a full understanding of multiple criteria decision making methodologies
  • Provides Python code for all methods
  • Features guidelines for selecting an appropriate method for specific decision problems
  • Compares key multiple criteria decision aid methods and approaches

Part of the book series: Springer Optimization and Its Applications (SOIA, volume 136)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xvii
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  2. TOPSIS

    • Jason Papathanasiou, Nikolaos Ploskas
    Pages 1-30

  3. VIKOR

    • Jason Papathanasiou, Nikolaos Ploskas
    Pages 31-55

  4. PROMETHEE

    • Jason Papathanasiou, Nikolaos Ploskas
    Pages 57-89

  5. SIR

    • Jason Papathanasiou, Nikolaos Ploskas
    Pages 91-107

  6. AHP

    • Jason Papathanasiou, Nikolaos Ploskas
    Pages 109-129

  7. Goal Programming

    • Jason Papathanasiou, Nikolaos Ploskas
    Pages 131-164

  8. Back Matter

    Pages 165-173
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Keywords

About this book

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, informationsystems, 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)


Authors and Affiliations

  • Department of Business Administration, University of Macedonia, Thessaloniki, Greece

    Jason Papathanasiou

  • Carnegie Mellon University, Pittsburgh, USA

    Nikolaos Ploskas

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