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Showing 4 results for Kamranrad
Multi Responses Optimization Through Game Theory Approach
Hamidreza Navidi, Amirhossein Amiri, Reza Kamranrad ,
Volume 25, Issue 3 (IJIEPR 2014)
Abstract
In this paper, a new approach based on game theory has been proposed to multi responses problem optimization. Game theory is a useful tool for decision making in the conflict of interests between intelligent players in order to select the best joint strategy for them through selecting the best joint desirability. Present research uses the game theory approach via definition of each response as each player and factors as strategies of each player. This approach cans determine the best predictor factor sets in order to obtain the best joint desirability of responses. For this aim, the signal to noise ratio(SN) index for each response have been calculated with considering the joint values of strategies then obtained SN ratios for each strategy is modeled in the game theory table. Finally, using Nash Equilibrium method, the best strategy which is the best values of predictor factors is determined. A real case and a numerical example are given to show the efficiency of the proposed method. In addition, the performance of the proposed method is compared with the VIKOR method.
Availability Prediction of the Repairable Equipment using Artificial Neural Network and Time Series Models
Hiwa Farughi, Ahmad Hakimi, Reza Kamranrad,
Volume 29, Issue 1 (IJIEPR 2018)
Abstract
In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA). Results based on comparative studies between four methods based on ANN and by considering the several conditions for the effective parameters in ANN show that, the generalized regression method is the best method for predicting the availability. Furthermore, results of the EWMA and three mentioned TSM are also show the better performance of MA model for predicting the availability values in future periods. 
A New approach to determine the Critical Factors of the Product Quality Optimization: A Structural Equation Modeling (A case study: hydrocarbon solvents based paints quality)
Hossein Khodami, Reza Kamranrad, Ehsan Mardan,
Volume 32, Issue 2 (IJIEPR 2021)
Abstract
Quality plays important role for sale in the market. To attain this, many industrial managements are eager to use optimization methods to develop product quality. In this study, by evaluating the relationships between product defects and the factors affecting them, ways to improve product quality are presented. Hence, in this paper, a Structural Equation Modeling (SEM) approach is developed to identify the critical factors affecting product quality in paints industry. To this aim, 94 different laboratory samples including hydrocarbon solvent-based paints are assessed. Smart PLS software is utilized to construct the optimized model to determine critical factors. Results show that the different defects affecting the quality of paint are interrelated. In other words, the creation of a flaw will cause other flaws. It has been found that paint surface mottling that depends on the amount of usage of the Bentonite gel, pigment quantity, and resin quality used in the paint formulation affect the other defects such as orange peeling and­ Cratering.

Single machine preemptive scheduling Considering Energy Consumption and Predicting Machine failures with Data Mining Approach
Ali Qorbani, Yousef Rabbani, Reza Kamranrad,
Volume 34, Issue 4 (IJIEPR 2023)
Abstract
Prediction of unexpected incidents and energy consumption are some industry issues and problems. Single machine scheduling with preemption and considering failures has been pointed out in this study. Its aim is to minimize earliness and tardiness penalties by using job expansion or compression methods. The present study solves this problem in two parts. The first part predicts failures and obtains some rules to correct the process, and the second includes the sequence of single-machine scheduling operations. The failure time is predicted using some machine learning algorithms includes: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, and k-nearest neighbors. Results of comparing the algorithms, indicate that the decision tree algorithm outperformed other algorithms with a probability of 70% in predicting failure. In the second part, the problem is scheduled considering these failures and machine idleness in a single-machine scheduling manner to achieve an optimal sequence, minimize energy consumption, and reduce failures. The mathematical model for this problem has been presented by considering processing time, machine idleness, release time, rotational speed and torque, failure time, and machine availability after repair and maintenance. The results of the model solving, concluded that the relevant mathematical model could schedule up to 8 jobs within a reasonable time and achieve an optimal sequence, which could reduce costs, energy consumption, and failures. Moreover, it is suggested that further studies use this approach for other types of scheduling, including parallel machine scheduling and flow job shop scheduling. Metaheuristic algorithms can be used for larger dimensions. 

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