maghsoud Amiri; mohsen shafiei nikabadi; Armin Jabbarzadeh
Abstract
In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply ...
Read More
In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear programming model is proposed to minimize supply chain costs. Then, for coping with hybrid uncertain parameters effectively, randomness and epistemic uncertainty, a novel robust stochastic-possibilistic programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry.
Akram Oveysiomran
Abstract
Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent ...
Read More
Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent in the literature and it is considered the main advantage of the proposed method. In order to train two layers MLP neural network, after presenting of error resilience, learning method was used. After neural network training, neural network performance is examined by using the test set. RMSE value for 15 test set equals 0/0269 which reflects the high accuracy of training network. The Sensitivity Analysis of the studied parameters which are the same inputs and outputs of Data Envelopment Analysis, with ten percent increase of parameter, compared to the prior one was carried out and output relative error average for neural network parameters was calculated. Based on the output relative error average, inputs and outputs were determined. By comparing the efficiency scores of regional electricity companies before and after reducing the number of variables, it is noticed that the number of efficient companies during the above four periods decreased from 50 percent to 11 percent. Finally, the neural network application in inputs and outputs selection of the regional electricity companies was unprecedented in the literature and this is the main advantage of this method.
Mohsan Shafiee Nick Abadi; Habib Farajpour Khanaposhtani; Hossein Eftekhari; Aliasghar Sadadadi
Volume 13, Issue 39 , January 2016, , Pages 35-62
Abstract
Today, companies have accepted that the maintenance and repairs are profitable business elements and therefore, the role of maintenance and repairs has become more important in modern manufacturing systems.Maintenance and repair play important roles in achieving organizational goals and improving the ...
Read More
Today, companies have accepted that the maintenance and repairs are profitable business elements and therefore, the role of maintenance and repairs has become more important in modern manufacturing systems.Maintenance and repair play important roles in achieving organizational goals and improving the indicators of reliability, reducing the equipment downtime, products quality, increasing productivity, safety equipment and etc. In this regard, the maintenance and repairs and their strategies have the particular importance in the industry. As a result, the main purpose of this study is selecting and ranking the best repairs and maintenance strategies using a combination of confirmatory factor analysis methods (FA), AHP and TOPSIS in oil refinery of Ray. According to that many variables such as safety, cost, added value, etc. are affective in choosing a maintenance and repairs strategy, in the present study at first has been identified these variables by the aid of literature review and experts opinion and then has been addressed to select the best maintenance strategy by AHP and TOPSIS techniques and try to offer suggestions for improving refinery maintenance system.
