Shahriari, MohammadrezaHosseinzadeh Lotfi, FarhadRahmaniperchkolaei, BijanTaeeb, ZohrehSaati, Saber2025-04-172025-04-172024Shahriari, M., Lotfi, F. H., Rahmaniperchkolaei, B., Taeeb, Z., & Saati, S. (2024). Data optimization and analysis. In Decision-Making Models (pp. 209-236). Academic Press.978-044316147-6, 978-044316148-3https://hdl.handle.net/20.500.12713/6242Efficient decision-making within any organization is not just a possibility, but a reality, thanks to the practicality of meticulous data analysis. This chapter delves deeply into an array of data analysis methods that prove to be invaluable in this pursuit. The central focus is directed toward the data envelopment analysis (DEA) technique. This potent tool, which serves as a cornerstone in evaluating the performance of a cluster of analogous decision-making units (DMUs), is not just a theoretical concept, but a practical solution. Throughout the chapter's course, we delve into a diverse range of models that encompass efficiency assessment, benchmarking, ranking, and advancement. Additionally, regression analysis is explored for each DMU. These models inherently accommodate multiple inputs and outputs, thereby facilitating a comprehensive evaluation. It becomes distinctly apparent that intricate DMUs or those governed by specific indicator conditions necessitate the employment of sophisticated models, as classical paradigms might fall short in such intricate scenarios. Furthermore, the chapter casts a spotlight on the support vector machine (SVM) method. SVM, a versatile approach for the classification of data points into discrete sets, is not just a single-use tool, but a versatile solution. It produces a set of rules that enable precise predictions regarding the categorization of a new data point within one of these predefined sets. By harnessing the power of SVM, organizations are not just limited to one type of data analysis, but can proficiently classify incoming data and derive informed decisions rooted in these discerning categorizations. This chapter provides readers with a profound understanding of the methodologies that underlie DEA and SVMs. These instrumental tools empower organizations to extract profound insights from their data reservoirs, thereby equipping them to navigate intricate decision terrains with unwavering assurance. © 2024 Elsevier Inc. All rights reserved.eninfo:eu-repo/semantics/closedAccessBenchmarkingData envelopment AnalysisEfficiencyRanking and Progress and RegressionSupport vector MachBook Chapter2092362-s2.0-8520288627810.1016/B978-0-443-16147-6.00028-1N/A