Multi-Attribute Decision-Making in Industrial Engineering
eBook - ePub

Multi-Attribute Decision-Making in Industrial Engineering

  1. 262 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Multi-Attribute Decision-Making in Industrial Engineering

About this book

Master the complexities of modern engineering management with this practical guide to multi-attribute decision-making, providing the advanced models and multi-criteria frameworks needed to resolve conflicting objectives and optimize industrial outcomes.

Multi-attribute decision-making (MADM) is a foundational area within contemporary decision science. Its ­theoretical frameworks and methodological approaches have been widely applied across disciplines such as industrial engineering, military affairs, economics, and management. This book provides a comprehensive and practical guide to this critical area in modern engineering management. Covering topics such as decision theory, multi-criteria analysis, and advanced decision-making models, the book explores how MADM techniques can be applied to solve real-world problems related to production planning, supply chain management, quality control, and operational efficiency. Whether dealing with conflicting objectives, limited resources, or complex trade-offs, this book serves as a valuable resource to enhance decision-making skills and optimize outcomes in industrial processes. With a clear focus on practical applications, it bridges the gap between theory and practice, making it ideal for both academic and professional use in the fields of industrial engineering and operations management.

Readers will find the volume:

  • Covers key Multiple Attribute Decision-Making methods like AHP, TOPSIS, and VIKOR with real-world industrial applications;
  • Provides step-by-step examples, making complex concepts easy to understand and apply;
  • Enhances productivity and accuracy in process optimization and resource allocation;
  • Includes case studies to bridge theory and practical implementation.

Audience

Academics, researchers, industrial engineers, operations researchers, supply chain analysts, project managers, and decision scientists who seek to enhance decision-making processes using structured multi-attribute methodologies.

Information

Year
2026
Print ISBN
9781394465033
eBook ISBN
9781394465040

Table of contents

  1. Cover
  2. Table of Contents
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Preface
  7. Acknowledgments
  8. 1 Introduction to Multi-Attribute Decision-Making Applications
  9. 2 COMPARALT – A Novel Approach for Multi-Criteria Decision Making
  10. 3 Multi-Attribute Decision-Making Applications in Optimizing Logistics and Supply Chain Management for Energy Storage Systems Techniques
  11. 4 Enhancing Casting Quality through Machine Learning and Artificial Intelligence: Predicting and Controlling Defects in Sand Castings
  12. 5 Developing Analytical Hierarchy Process Framework as Multi Criteria Decision Making Tool for Analysing Green Supply Chain Management Practices
  13. 6 Multi-Attribute Decision Making in Human Factors and Ergonomics
  14. 7 Measuring Service Quality Using Multi Attribute Decision Making-Case of Healthcare Sector
  15. 8 Development and Implementation of a Quality Cost Management System in Advanced Manufacturing Environments
  16. 9 Application of Analytic Hierarchy Process (AHP) for Identifying Optimal Lean Tools in Testing Laboratories
  17. 10 Identification and Prioritizing the Performance Metrics for Textile Supply Chain: Multi-Attribute Decision Making Approach
  18. 11 Application of Multi-Attribute Decision-Making in Manufacturing Systems
  19. Index
  20. End User License Agreement

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Yes, you can access Multi-Attribute Decision-Making in Industrial Engineering by Pranav Charkha,Vijayshri Mahobiya,Santosh Jaju,Vinit Gupta,Sandip Kunar in PDF and/or ePUB format, as well as other popular books in Technologie et ingénierie & Ingénierie industrielle. We have over 1.5 million books available in our catalogue for you to explore.