Fuzzy Decision-Making Algorithms: Mathematical Optimization, Metaheuristic Optimization, Supply Chain Management

This book looks at how to combine metaheuristic optimization algorithms and fuzzy decision-making methods to improve the sustainability and effectiveness of supply chains. Mathematical and metaheuristic optimization methods based on natural processes, combined with fuzzy decision-making, enable the construction of models that can effectively address the multifaceted issues of contemporary supply chains. This book offers a variety of decision-making techniques, including entropy measures, distance measures, coefficient correlation, aggregating operators, TOPSIS, EDAS, and more. This book overcomes the drawbacks of existing fuzzy decision-making methods. The book also presents case studies that demonstrate the application of metaheuristic decision-making algorithms to improve the sustainability and viability of supply chains. One such notable example is green supplier selection in supply chain management. The changing supply chain management landscape offers many areas for future research, like investigating the intersection of mathematical and metaheuristic fuzzy decision-making algorithms with new technologies such as the Internet of Things (IoT) and artificial intelligence, which can create more responsive and adaptive supply chains.