Optimization and computing using intelligent data-driven approaches for decision-making
Sourena Rahmani, Hamed Aghalar, Sahel Jebreili, Alireza Goli
Abstract
This chapter explores the integration of optimization techniques with intelligent data-driven approaches as a solution to the intricate challenges faced by organizations in today’s data-rich environment. The chapter emphasizes the significance of optimization in decision-making and highlights the role of data-driven approaches, such as machine learning, artificial intelligence, and big data analytics, in enhancing decision quality through the intelligent utilization of data. The chapter examines various commonly used optimization algorithms in intelligent data-driven decision-making, including evolutionary algorithms, metaheuristic algorithms, and swarm intelligence techniques. It discusses their strengths and limitations across different application domains, shedding light on their potential benefits for organizations. Moreover, the chapter explores the integration of optimization and computing approaches and demonstrates how data-driven methodologies can address complex optimization problems. It showcases the synergy between optimization and intelligent computing, presenting their combined potential in tackling real-world decision-making challenges. The chapter supports its findings with practical case studies and examples from diverse domains like supply chain management, finance, healthcare, and engineering. These case studies demonstrate the tangible benefits of incorporating optimization and intelligent computing techniques in decision-making, including improved efficiency, reduced costs, and enhanced outcomes. Overall, this chapter provides valuable insights for researchers, practitioners, and decision-makers seeking to leverage data-driven optimization techniques for informed and impactful decision-making. Its comprehensive exploration of optimization and computing using intelligent data-driven approaches addresses the complex decision-making needs of organizations in today’s data-driven world.