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Öğe 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, MahnazPurpose: 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.Öğe Data optimization and analysis(Elsevier, 2024) Shahriari, Mohammadreza; Hosseinzadeh Lotfi, Farhad; Rahmaniperchkolaei, Bijan; Taeeb, Zohreh; Saati, SaberEfficient 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.Öğe Discrete and combinatorial optimization(Elsevier, 2024) Rahmaniperchkolaei, Bijan; Taeeb, Zohreh; Shahriari, Mohammadreza; Lotfi, Farhad Hosseinzadeh; Saati, SaberIn the realm of practical scenarios, numerous complex situations inherently align with the framework of integer programming (IP). These real-life challenges emerge when the linear programming assumption of divisibility proves inapplicable. An integer programming problem manifests as an extension of linear programming (LP), wherein some or all decision variables are constrained to non-negative integer values. However, the unfortunate reality is that solving integer programming problems tends to be considerably more intricate than addressing standard linear programming challenges. A plethora of vital optimization problems within diverse domains find their most fitting representation through either graphical or grid-based models. These models offer an intuitive approach to understanding and solving intricate optimization quandaries. The focus of this chapter lies in the exploration of integer programming problems and the transportation problem, which emerges as a distinct facet of linear programming. The transportation problem stands as one among the specialized structures of linear programming, garnering extensive applicability in real-world scenarios. It serves as a pivotal tool for efficiently allocating resources, optimizing supply chains, and devising strategies for distribution and logistics. This chapter embarks on a journey to decipher the intricacies of integer programming, uncovering its significance in encapsulating real-life dilemmas where discrete decision-making is fundamental. By delving into the nuances of the transportation problem, we gain insights into a practical manifestation of linear programming's potential, further enriching our understanding of optimization techniques in the context of real-world complexities. © 2024 Elsevier Inc. All rights reserved.Öğe Effects of Size of Decision-making Units on Allocating Fixed Costs in Supply Chains(Materials and Energy Research Center, 2024) Emami L. a; Lotfi, F. Hosseinzadeh; Rostami-Malkhalifeha M.In the modern world, effective and successful supply chain management is among the most significant strategies for all organizations and public/private companies. Nowadays, recommending new tools and attitudes to improve supply chain efficiency is a challenging topic in organizational management. Another important goal of organizational management is to allocate costs logically and conveniently. Even though there are numerous studies on fixed costs and many tools for addressing this issue, data envelopment analysis (DEA) is a powerful technique for fair allocation. Therefore, the present study aimed to investigate the allocation of fixed costs in four-level supply chains using DEA models. The proposed model was examined by implementing a common set of weights and cost allocations according to the size of the decision-making unit. Additionally, the goal programming technique was applied to minimize the deviation between efficient allocation and size-based allocation. The proposed model is linear and provides a unique fixed allocation for each stage of the supply chain. Given that this model is always feasible, it can always provide an optimal allocation for each stage of the supply chain in different industries. In this paper, we focused on the allocation of personnel costs in a cement supply chain in Iran. The results show that the allocation amount to the customer stage (fourth stage) of the supply chain is much higher than other stages and the lowest allocation is obtained in the supplier stage (first stage). ©2024 The author(s).Öğe Improving technical efficiency in data envelopment analysis for efficient firms: A case on Chinese banks(Elsevier Inc., 2024) Amirteimoori, Alireza; Allahviranloo, TofighData Envelopment Analysis (DEA) as a data-oriented benchmarking tool is considered a powerful and promising instrument for performance evaluation in various application areas. In DEA, the set of all decision-making units (DMUs) is divided into efficient and inefficient subsets. Inefficient DMUs are improved by reducing the input and/or increasing the output, and as far as we know, efficient DMUs are abandoned with the conclusion that they are all technically and relatively efficient, and no further analysis has been suggested in the literature. In this article, we first show that there is a gap between the actual efficiency and the efficiency estimated using benchmarking tools such as DEA. This means that there is no guarantee that the efficient DMUs characterized by DEA are really efficient. Thus, there is a gap in improving the technical efficiency of efficient DMUs. In this paper, we attempt to close this gap by introducing a method to improve efficient DMUs. First, we introduce a random variable as a corrector of efficiency evaluation, and then an inverse DEA model (IDEA) is proposed to improve efficient DMUs. To demonstrate the actual applicability of the proposed approach, we present an illustrative empirical application using 106 Chinese bank data from 2021. © 2024 Elsevier Inc.Öğe A Hybrid Approach to Analyzing Factors Influencing International Student Retention in Turkish Higher Education: Integrating Fuzzy DEMATEL, Machine Learning, and Statistical Methods(Research Expansion Alliance (REA), 2025) Usta Ergün, Serra BegümRetention of international students poses a significant challenge for higher education institutions due to its social and economic consequences. This research investigates the connections between important variables, including intention to leave, integration commitment, educational commitment, student satisfaction, and challenges faced. A comprehensive model was utilized to examine how these factors influence retention, with a specific focus on the impact of demographic factors. Data was gathered from 2,736 international students enrolled in universities in Turkey and analyzed using IBM SPSS 26 and R software. The findings reveal that the intention to leave exhibits a negative correlation with integration commitment, educational commitment, and satisfaction, while showing a positive association with encountered difficulties. Furthermore, demographic factors such as language skills, quality of life, and family support play a significant role in influencing retention. The results of the hypothesis testing were further substantiated through the use of the fuzzy DEMATEL method, reinforcing the connections among the key variables. The outcomes indicate that the choice to leave is a complex, multifactorial issue, significantly influenced by both personal and institutional factors. As a result, it is essential to develop strategies that assist international students in their academic, cultural, and social environments to improve retention. © 2025, Research Expansion Alliance (REA). All rights reserved.Öğe Efficiency analysis in bi-level on fuzzy input and output(Elsevier inc., 2025) Ghaziyani, Kh; Lotfi, F. Hosseinzadeh; Kordrostami, Sohrab; Amirteimoori, AlirezaTo enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA's 'black box' approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell's method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model's parameters.Öğe A cutting-edge data envelopment analysis model for measuring sustainable supplier performance like never before(Elsevier, 2024) Zoghi, Amin; Lotfi, Farhad Hosseinzadeh; Saen, Reza Farzipoor; Saati, SaberOne of the challenges for suppliers is to increase their market share due to the limited target market. In other words, in the supply chain, the demand for suppliers' products is limited. Therefore, suppliers produce a determined share of the required amount by producers. However, when it comes to the share in the amount of output among suppliers, the output of suppliers in this indicator is interdependent. In the classical data envelopment analysis (DEA) models, there are no models that assess the suppliers according to the dependence of at least one output on each other. In this paper, a model is presented that can assess sustainable suppliers in the presence of interdependent output among suppliers by using DEA models and their extension. It can also determine the total benchmarks of decision making units (DMUs) in such a way that satisfies the interdependent output constraint. In other words, benchmarks are not determined independently. Ultimately, an approach is presented to determine efficient projections for inefficient DMUs by considering the concept of interdependent output. To represent the applicability of our proposed model, a dataset for two consecutive years including 32 sustainable suppliers with consideration of interdependent output has been implemented using the model presented in this paper. The resulting sustainability has been compared with a classical model.
