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Showing 3 results for Hosseinzadeh Lotfi
Hosseinzadeh Lotfi, Noora, Jahanshahloo,
Volume 2, Issue 1 (vol 2. No 1 2010)
Abstract
We suggest a method for finding the non-dominated points of the production possibility set (PPS) with variable returns to scale (VRS) technology in data envelopment analysis (DEA). We present a multiobjective linear programming (MOLP) problem whose feasible region is the same as the PPS under variable returns to scale for generating non-dominated points. We demonstrate that Pareto solutions of the MOLP produce efficient units in DEA, and vice versa. We solve the MOLP problem by using a finite number of weights which are extreme rays of the cone generated by the efficient solutions. We obtain new efficient points by changing weights, and thus the efficient solutions set is produced.
Mrs. S. Madadi, Dr. F. Hosseinzadeh Lotfi, Dr. M. Rostamy-Malkhalifeh, Dr. M. Fallah Jelodar,
Volume 9, Issue 1 (7-2018)
Abstract
Resource allocation is a problem that commonly appears in organization with a centralized decision making (CDM), who controls the units. The aim of central decision making is to allocate resources in such a way that the organization get the most benefit. Some Data Envelopment Analysis (DEA) researchers presented DEA-based resource allocation models by paying attention to energy saving and environmental pollution reduction. In this paper, we expanded a resource allocation model for 25 branches of an Iranian Tejarat bank, so that determined how much decision making (DM) can save on energy and manpower hours, so that undesirable outputs like non-performing loans are significantly reduced in a way that achieve the minimum reduction of desirable outputs while unchanged the performance of each unit after re-allocation. The result of the implementation of the model shows that in total with a 10% and 23% reduction in staff and costs respectively can result in the 0.09% reduction of deposits and 56% of non-performing loans.
Dr. Mohammad Fallah, Dr. Farhad Hosseinzadeh Lotfi, Mohammad Mehdi Hosseinzadeh,
Volume 11, Issue 1 (9-2020)
Abstract
Using the experiences of successful and unsuccessful companies can be a criterion for predicting the situation of emerging companies. Each company can have a vector include both financial and non-financial characteristics. Accordingly, for an active or emerging company, it is possible to determine the characteristic vector and predict which group it is likely to belong to. The techniques used in this research are discriminant analysis and data envelopment analysis. Based on this technique, discriminant functions are designed to separate known sets. The main idea for finding discriminant functions is from data envelopment analysis, which makes a limit of efficiency for separating efficient units from inefficient ones. The discriminant functions of this method are used to predict the state of the company. Hyper planes are obtained as discriminant functions to separate companies. These hyper planes are based on multiple indicators. Each of these indicators can also apply in certain situations. The modeling used in this paper was used on oil companies listed on the Iran Stock Exchange. 15 indicators and criteria have been defined for each company. The data were for 2015 and 2016, and the number of oil companies was 18, of which 9 were successful and 9 were bankrupt. In this paper, with the help of data envelopment analysis and discriminant analysis, a new modeling was designed to find hyper planes for separating two sets. Modeling has been performed based on the different criteria that have existed, and each one applies in certain circumstances. In the following, the properties of the designed model are expressed and proved. The specific conditions of the criteria have become limitations that have been added to the multiplicative form of the designed model.
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