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Showing 4 results for Rezaee

M.r. AliRezaee, S.a Mir-Hassani,
Volume 17, Issue 4 (IJES 2006)

In the evaluation of non-efficient units by Data Envelopment Analysis (DEA) referenced Decision Making Units (DMU’s) have an important role. Unfortunately DMU’s with extra ordinary output can lead to a monopoly in a reference set, the fact called abnormality due to the outliers' data. In this paper, we introduce a DEA model for evaluating DMU’s under this circumstance. The layer model can result in a ranking for DMU’s and obtain an improving strategy leading to a better layer.

M. Kargari, Z. Rezaee, H. Khademi Zare ,
Volume 18, Issue 3 (International Journal of Engineering 2007)

 Abstract : In this paper a meta-heuristic approach has been presented to solve lot-size determination problems in a complex multi-stage production planning problems with production capacity constraint. This type of problems has multiple products with sequential production processes which are manufactured in different periods to meet customer’s demand. By determining the decision variables, machinery production capacity and customer’s demand, an integer linear program with the objective function of minimization of total costs of set-up, inventory and production is achieved. In the first step, the original problem is decomposed to several sub-problems using a heuristic approach based on the limited resource Lagrange multiplier. Thus, each sub-problem can be solved using one of the easier methods. In the second step, through combining the genetic algorithm with one of the neighborhood search techniques, a new approach has been developed for the sub-problems. In the third step, to obtain a better result, resource leveling is performed for the smaller problems using a heuristic algorithm. Using this method, each product’s lot-size is determined through several steps. This paper’s propositions have been studied and verified through considerable empirical experiments.


Dr. Mustafa Jahnagoshai Rezaee, Dr. Alireza Moini,
Volume 26, Issue 4 (IJIEPR 2015)

Data envelopment analysis (DEA) and balanced scorecard (BSC) are two well-known approaches for measuring performance of decision making units (DMUs). BSC is especially applied with quality measures, whereas, when the quantity measures are used to evaluate, DEA is more appropriate. In the real-world, DMUs usually have complex structures such as network structures. One of the well-known network structures is two-stage processes with intermediate measures. In this structure, there are two stages and each stage uses inputs to produce outputs separately where the first stage outputs are inputs for the second stage. This paper deals with integrated DEA and game theory approaches for evaluating two-stage processes. In addition, it is an extension of DEA model based on BSC perspectives. BSC is used to categorize the efficiency measures under two-stage process. Furthermore, we propose a two-stage DEA model with considering leader-follower structure and including multiple sub stages in the follower stage. To determine importance of each category of measures in a competitive environment, cooperative and non-cooperative game approaches are used. A case study for measuring performance of power plants in Iran is presented to show the abilities of the proposed approach.


Mohammad Mehdi Dehdar, Mustafa Jahangoshai Rezaee, Marzieh Zarinbal, Hamidreza Izadbakhsh,
Volume 29, Issue 4 (IJIEPR 2018)

Human-based quality control reduces the accuracy of this process. Also, the speed of decision making in some industries is very important. For removing these limitations in human-based quality control, in this paper, the design of an expert system for automatic and intelligent quality control is investigated. In fact, using an intelligent system, the accuracy in quality control is increased. It requires the knowledge of experts in quality control and design of expert systems based on the knowledge and information provided by human and equipment. For this purpose, Fuzzy Inference System (FIS) and Image Processing approach are integrated. In this expert system, the input information is the images of the products and the results of processing on images for quality control are as output. At first, they may be noisy images; the pre-processing is done and then a fuzzy system is used to be processed. In this fuzzy system, according to the images, the rules are designed to extract the specific features that are required. At second, after the required attributes are extracted, the control chart is used in terms of quality. Furthermore, the empirical case study of copper rods industry is presented to show the abilities of the proposed approach.

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