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6. Preface

Author

Lotfi, Farhad Hosseinzadeh, primary, Sanei, Masoud, additional, Hosseinzadeh, Ali Asghar, additional, Niroomand, Sadegh, additional, and Mahmoodirad, Ali, additional

Published
2023
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7. Uncertain theories

Author

Lotfi, Farhad Hosseinzadeh, primary, Sanei, Masoud, additional, Hosseinzadeh, Ali Asghar, additional, Niroomand, Sadegh, additional, and Mahmoodirad, Ali, additional

Published
2023
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8. Fuzzy data envelopment analysis

Author

Lotfi, Farhad Hosseinzadeh, primary, Sanei, Masoud, additional, Hosseinzadeh, Ali Asghar, additional, Niroomand, Sadegh, additional, and Mahmoodirad, Ali, additional

Published
2023
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28. Multi-Objective Planning Model for Optimizing the Portfolio of Financial and Credit Institutions: A Case Study of Sistan and Baluchestan Agricultural Bank.

Author

Ghalehno, Rouhollah Kiani, Niroomand, Sadegh, Didekhani, Hosein, and Mahmoodirad, Ali

Abstract

Purpose: Optimizing the negative balance of the financial portfolio of branches by observing the limits defined in the banking system of Iran. Methodology: In recent years, several models have been proposed for the investment portfolio. In banks, Fundraising operations are carried out in parallel with investments. Attracting deposits and repaying loans are the main pillars of investment and form the basis of the resource and expenditure portfolio in the bank. In this research, a multi-objective planning model is designed to maximize returns and minimize risk. Findings: The approach of the problem is such that by taking administrative and personnel costs and interest rates on deposits and facilities and exchange rates of the domestic market can offer a variety of portfolios. The branches select the appropriate portfolio as the goal and work plan according to their requirements. Originality/Value: Due to the nature of the problem, which is hard nonlinear, the model is solved using NSGA-II evolutionary algorithm. The output of solving the problem is a set of optimal solutions on the Pareto frontier. Each of the portfolios is a strategic choice for the decision-maker, according to the level of return and risk. [ABSTRACT FROM AUTHOR]

Published
2022

41. Uncertain SBM data envelopment analysis model: A case study in Iranian banks.

Author

Jamshidi, Mohammad, Sanei, Masoud, Mahmoodirad, Ali, Lotfi, Farhad Hoseinzadeh, and Tohidi, Ghasem

Subjects
DATA envelopment analysis, CASE studies, EXPECTED returns
Abstract

Data envelopment analysis (DEA) is a strong analytical tool and methodology for evaluating the relative efficiency of decision‐making units (DMUs). The DEA models require inputs and outputs, which are equipped with precise information. However, the real‐world inputs and outputs are probably so changeable and complicated that cannot be measured accurately. Consequently, this conflict leads to the analysis of uncertain DEA models. This paper aims to analyze the slacks‐based measure (SBM) model in an uncertain environment where the uncertain inputs and outputs are belief degree‐based uncertainty. The belief degree‐based uncertainty is useful for the cases in which no historical information of an uncertain event is available. As a solution methodology, the uncertain SBM model is converted to a crisp form by using three approaches separately: expected value model, expected value and chance‐constrained model, and dependent chance‐constrained model. Additionally, an alternative uncertain model is introduced to reveal differences of DMUs rankings in three crisp methods. Finally, an applied scenario regarding the Iranian banking system is documented to present the new models. [ABSTRACT FROM AUTHOR]

Published
2021
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42. Metaheuristics for data envelopment analysis problems

Author

Molla-Alizadeh-Zavardehi, Saber, Mahmoodirad, Ali, Sanei, Masoud, Niroomand, Sadegh, and Banihashemi, Shokoofeh

Abstract

In conventional data envelopment analysis (DEA) models, some inputs and outputs may lose their weights in the process of measuring the relative efficiency of DMUs and it leads to ignore corresponding inputs and outputs. Also, using the DEA for a large dataset needs to have a long time with a high-speed computer. In order to solve the problem, genetic algorithm (GA) and differential evolution (DE) algorithm are adopted and proposed in this paper. The GA is used in previous papers but here some operators are used for the first time. Besides, DE algorithm is adopted and used to solve the problem via new operators which are first introduced in this paper. Moreover, a plan is utilised to generate test problems in different sizes in this work. Since the crossover and mutation operators have significant impacts on the algorithms’ performance, all the operators and parameters are calibrated by means of the Taguchi experimental design in order to improve their performances. The performances of the proposed algorithms are then evaluated by comparing their solutions based on the presented instances. Finally, the impacts of increasing the problem size on the performance of our proposed algorithms are investigated.

Published
2021
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