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El. knyga: Decision-Making Models and Applications in Manufacturing Environments

Edited by , Edited by , Edited by (MCKV Institute of Engineering, Liluah)
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Multi-criteria decision-making (MCDM) has gained vast popularity for its ability to help make decisions in the presence of various similar and conflicting choices.This new volume applies the MCDM theory to solving problems and challenges in manufacturing environments. It discusses using MCDM computational methods to evaluate and select the most optimal solution or method for real-world, real-time manufacutring engineering issues. It details the decision-making process in relation materials selection; identification, assessment, and evaluation of risk; sustainability assessment; selection of green suppliers; and more.

The chapter authors demonstrate the application of myriad MCDM techniques in decision-making, including MADM (multiple attribute decision-making), DEA (data envelopment analysis), fuzzy TOPSIS (technique for order preference by similarities to ideal solution), fuzzy-VIKOR (multicriteria optimization and compromise solution); MOORA (multi-objective optimization on the basis of ratio analysis), EWM (entropy weight method), (AHP) analytic hierarchy process, TODIM (TOmada de Decisao Interativa Multicriterio), and others. The volume illustrates these MCDM models in several industries and industrial processes, including for experimental analysis and optimization of drilling of glass fiber reinforced plastic, in the textile industries, for selection of refrigerants for domestic applications, and others.



Applies the multi-criteria decision-making theory to solving problems and challenges in manufacturing environments, using MCDM computational methods to evaluate and select the most optimal solution or method to real-world manufacturing engineering issues.

1. MCDM, DEA, and Their Relationship in Material Selection
2. Identification, Assessment, and Evaluation of Risk in Various Manufacturing Industries
3. Experimental Analysis and Optimization of Drilling of Glass Fiber Reinforced Plastics Composites Using MADM Methodology: Utility Concept
4. Identification of Problems and Their Prioritization in Textile Industries Using MADM Techniques: Fuzzy-TOPSIS and Fuzzy-VIKOR Methods
5. Selection of Refrigerants for Domestic Applications Using MADM Techniques
6. Comparative Study of MCDM Techniques: TOPSIS, VIKOR, and MOORA Methods Integrated with EWM Method for Vendor Selection in the Manufacturing Industry
7. Selection of Cutting Fluids for Machining Titanium Alloys Using MCDM Methods
8. Decision-Making Framework for Sustainability Assessment of Manufacturing
9. ARAS-Based Selection of Optimal Specimen Among PRPs and Its Validation Using MVGFD
10. Optimization of PMEDM Process Parameters for MRR, TWR, RA, and HV Using Taguchi Method and Grey Relational Analysis for Die Steel Materials
11. Selection of Optimal Rapid Prototyping Process Using Multi-Variant MCDM-Based Approaches
12. An Investigation into the Effect of Criteria Interaction on the TOmada de Decisao Interativa Multicriterio (TODIM) Method
13. Selection of Air-Conditioning System via a Value Engineering Management Tool
14. On the Comparison of MCDM Techniques for the Selection of Polymer-Based Additive Manufacturing Processes
15. Strength Analysis of Vibratory Friction Stir Welded AA 2024 and AA 7075 Dissimilar Metals
16. Performance Evaluation of Conveyors through Fuzzy AHP-Based Fuzzy Grey Theory Analysis under MCDM Environment
17. An Innovative Computational Approach for Processing and Characterization of Polymer Welding
18. Performance Analysis of Material Handling Device Using Integrated COPRAS-G and Fuzzy AHP
19. Role of IoT, Big Data, and AI in the Manufacturing and Industrial Sectors During COVID-19 Pandemic: An ISM Approach
20. An ISM Approach Among the Key Factors of SCM for Industry 4.0 Towards Sustainability in Indian Manufacturing Sectors
21. Selection of Green Supplier Using Integrated Multi-Criteria Optimization Method: With Special Reference to Plastic Extrusion and Vacuum Forming Companies in India

Pushpdant Jain, PhD, is an Assistant Professor in the School of Mechanical Engineering at VIT Bhopal University, India. Prior to academia, he served in industry for six years. He has published research papers in international peer-reviewed journals and has published several book chapters. Dr. Jain has participated in international conferences, is an active member of the European Society of Bio-Mechanics and life member of the International Association of Engineers, Indian Institution of Industrial Engineering, and Indian Society for Technical Education. He was named Research Scholar of the Year (2018) by NIT Rourkela for his PhD work.

Kumar Abhishek, PhD, is an Assistant Professor in the Mechanical and Aero-Space Engineering Department at the National Institute of Infrastructure, Technology, Research and Management, Ahmedabad, India. His area of research mainly focuses on manufacturing and industrial engineering, modeling and optimization of production processes, and composite machining. He has many publications in reputed journals and international conference publications to his credit. He organizes a workshop every consecutive year titled MOOMESAMulti Objective Optimization Methods for Engineering and Scientific Applications.

Prasenjit Chatterjee, PhD, is Dean (Research and Consultancy) at the MCKV Institute of Engineering, West Bengal, India. He has over 100 research papers published in international journals and peer-reviewed conferences. He has authored and edited more than 15 books on intelligent decision-making, supply chain management, optimization techniques, risk, and sustainability modelling. Dr. Chatterjee is the Editor-in-Chief of the Journal of Decision Analytics and Intelligent Computing as well as a series editor for several book series. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods: Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).
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