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    Data optimization and analysis
    (Elsevier, 2024) Shahriari, Mohammadreza; Hosseinzadeh Lotfi, Farhad; Rahmaniperchkolaei, Bijan; Taeeb, Zohreh; Saati, Saber
    Efficient 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.
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    Efficiency decomposition in three-stage data envelopment analysis with undesirable and returnable factors
    (Emerald Publishing, 2024) Malmir, Mohammad; Hosseinzadeh Lotfi, Farhad; Kazemi Matin, Reza; Ahadzadeh Namin, Mahnaz
    Purpose: The purpose of this paper is to evaluate the efficiency of a series network system with undesirable and unreturnable simultaneously. Design/methodology/approach: The research was conducted by applying data envelopment analysis (DEA) approach to measure the efficiency score of a system and substages with an undesirable output of the second and third stages separately. For each case, new production technology was introduced, and based on them, novel DEA models were proposed. Findings: One of the most important issues in the development of a country is the banking industry. In this study, 51 branches of commercial banks as a three-stage system with undesirable and unreturnable outputs in the second stage are considered. Then, the efficiency of each branch and substages is measured by using proposed models. Originality/value: The efficiency of a three-stage network in the presence of undesirable and unreturnable outputs was assessed. In this model, Kousmanen’s technology was used. © 2024, Emerald Publishing Limited.
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    Mathematical programming
    (Elsevier, 2024) Hosseinzadeh Lotfi, Farhad; Saati, Saber; Shahriari, Mohammadreza; Rahmaniperchkolaei, Bijan; Taeeb, Zohreh
    Optimization stands as an exceptional cornerstone, harnessed extensively and with a resounding impact to confront the multifaceted challenges of the tangible world. Its expansive repertoire of applications, complemented by its innate ability to unfurl optimal solutions, has bestowed upon it an inherent allure, captivating a diverse spectrum of enthusiasts traversing the intricate landscape of research. Spanning domains as varied as the fundamental sciences, engineering marvels, strategic management paradigms, agricultural landscapes, and the ecological tapestry of environmental studies, optimization resonates as a unifying force in problem-solving endeavors. Nestled within the confines of this chapter is a captivating odyssey, a journey that navigates through the bedrock of optimization's essence. We embark upon this voyage by peeling back the layers of fundamental concepts that lay the groundwork for this dynamic field. From this foundational standpoint, we plunge headfirst into the realm of optimization problems, where two distinctive categories beckon: the structured world of linear programming and the intricate enigma of nonlinear programming. As we unravel the mysteries that enshroud these two archetypes, we simultaneously unveil the symphony of solution algorithms meticulously tailored to navigate each unique category's labyrinthine complexities. In our pursuit to instill a comprehensive comprehension, we seamlessly weave tangible examples throughout the narrative tapestry. These instances of real-world conundrums serve as eloquent companions to the theoretical frameworks, acting as guiding beacons to illuminate the path for readers. Through this harmonious marriage of theory and application, we aspire to bestow upon our readers the twin gifts of knowledge and proficiency—a twin that empowers them to not only navigate the intricate terrain of optimization but also to rise as adept problem solvers, armed with the analytical acumen and innovative strategies requisite to surmount the most formidable challenges that reality presents. © 2024 Elsevier Inc. All rights reserved.
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    Matrix-based network data envelopment analysis: A common set of weights approach
    (Elsevier Ltd, 2024) Peykani, Pejman; Seyed Esmaeili, Fatemeh Sadat; Pishvaee, Mir Saman; Rostamy Malkhalifeh, Mohsen; Hosseinzadeh Lotfi, Farhad
    Performance measurement of decision-making units (DMUs) with network structure is one of the main challenges in data envelopment analysis (DEA) field. The main purpose of this paper is to propose a novel network data envelopment analysis (NDEA) approach based on matrix of efficiency, common set of weights (CSW), multi-objective programming (MOP), and goal programming (GP) technique for performance measurement of peer DMUs in two-stage network structure. The advantages of the proposed NDEA approach can be summarized as follows: comparing all DMUs and sub-DMUs on the same base, considering all internal structures and relations and capability to extending this for all network structures, linearity of the proposed models, unique efficiency decomposing without any need to consider multiplicative, additive or leader-follower relations between overall and stages efficiency. To illustrate the usefulness and applicability of the proposed approach we applied it to a real application of non-life insurance companies in Taiwan. © 2024 Elsevier Ltd
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    Target setting and benchmarking by the possibility of selecting the closest alternative benchmark
    (EDP Sciences, 2025) Kameli, Mortaza; Daneshian, Behrouz; Modarres Khiyabani, Farzin; Hosseinzadeh Lotfi, Farhad
    Data envelopment analysis (DEA) stands out from other performance evaluation methods because it focuses on providing benchmarks. Benchmarking plays a vital role in improvement the performance of inefficient units, especially when classical models are combined with managerial perspectives, potentially resulting in a more feasible benchmark or target. Sometimes, choosing certain efficient units in the system as a benchmark and target isna't feasible from a management standpoint for inefficient units. This happens for various reasons, including the exclusivity of an efficient unit, the outlier status of an efficient unit, insufficient access to resources, or discrepancies with reality. Such units shouldna't be chosen as benchmark and target for inefficient units within the system. Therefore, disregarding such units in the benchmarking process based on the managera, s preference and opinion can contribute to better alignment with reality. Implementing this strategy reduces the disparity between actual and efficient performance. This paper presents a model designed to address these considerations. The model takes into consideration managerial opinions, enabling managers to identify and ignore specific efficient units during benchmarking. It then proposes alternative benchmarks closest in similarity to improve efficiency for the inefficient units. © The authors. Published by EDP Sciences, ROADEF, SMAI 2024.