Multi-Attribute Decision-Making in Industrial Engineering, Gebunden
Multi-Attribute Decision-Making in Industrial Engineering
(soweit verfügbar beim Lieferanten)
- Herausgeber:
- Pranav Charkha, Sandip Kunar, Santosh Jaju, Vijayshri Mahobiya, Vinit Gupta
- Verlag:
- Wiley, 08/2026
- Einband:
- Gebunden
- Sprache:
- Englisch
- ISBN-13:
- 9781394465033
- Artikelnummer:
- 12758469
- Umfang:
- 288 Seiten
- Erscheinungstermin:
- 10.8.2026
- Hinweis
-
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
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.
