Multi Criteria Decision Making And Optimum Design With Machine Learning
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Multi-Criteria Decision-Making and Optimum Design with Machine Learning
As multicriteria decision-making (MCDM) continues to grow and evolve, machine learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design. Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is a comprehensive resource that bridges the gap between ML and MCDM. It offers a practical approach by demonstrating the application of ML and MCDM algorithms to real-world problems. Through case studies and examples, it showcases the effectiveness of these techniques in optimal design. The book also provides a comparative analysis of conventional MCDM algorithms and machine learning techniques, enabling readers to make informed decisions about their use in different scenarios. It also delves into emerging trends, providing insights into future directions and potential opportunities. The book covers a wide range of topics, including the definition of optimal design, MCDM algorithms, supervised and unsupervised ML techniques, deep learning techniques, and more, making it a valuable resource for professionals and researchers in various fields. Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is designed for professionals, researchers, and practitioners in engineering, computer science, sustainability, and related fields. It is also a valuable resource for students and academics who wish to expand their knowledge of machine learning applications in multicriteria decision-making. By offering a blend of theoretical insights and practical examples, this guide aims to inspire further research and application of machine learning in multidimensional decision-making environments.
Multi-criteria Decision-making and Optimum Design with Machine Learning
"As the field of Multi-Criteria Decision-Making (MCDM) continues to grow and evolve, Machine Learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design. This practical guide is intended for researchers, practitioners, and students interested in using Machine Learning and Multiple-Criteria Decision-Making techniques for optimum design"--
Multiple-Criteria Decision-Making (MCDM) Techniques and Statistics in Marketing
Marketing has become increasingly popular, utilizing multi-criteria decision making (MCDM) methods and statistics to create robust frameworks for making informed and strategic decisions. Computational and modeling breakthroughs have resulted in a significant increase in the use of MCDM methods in marketing in the past decade, making it an ideal solution for many marketing problems. Statistics can now be used to conduct MCDM analyses on a variety of marketing problems, including new product introduction and pricing, using multiple data sources. Businesses can make more informed, strategic, and effective decisions by using MCDM methods and statistical analysis in marketing. By using these tools, marketers can improve market performance and competitive advantage by optimizing product development, pricing strategies, market segmentation, and campaign effectiveness. Multiple-Criteria Decision-Making (MCDM) Techniques and Statistics in Marketing explores the effects of MCDM techniques on marketing practices. It covers a wide range of statistics and research to examine MCDM in shaping modern consumer science. This book covers topics such as management science, product development, and consumer behavior, and is a useful resource for marketers, business owners, data scientists, academicians, and researchers.