Dr Mojtaba Heydar

The complexity of large-scale projects has led to numerous risks in their life cycle. This paper presents a new risk evaluation approach in order to rank the high risks in large-scale projects and improve the performance of these projects. It is based on the fuzzy set theory that is an effective tool to handle uncertainty. It is also based on an extended VIKOR method that is one of the well-known multiple criteria decision-making (MCDM) methods. The proposed decision-making approach integrates knowledge and experience acquired from professional experts, since they perform the risk identification and also the subjective judgments of the performance rating for high risks in terms of conflicting criteria, including probability, impact, quickness of reaction toward risk, event measure quantity and event capability criteria. The most notable difference of the proposed VIKOR method with its traditional version is just the use of fuzzy decision-matrix data to calculate the ranking index without the need to ask the experts. Finally, the proposed approach is illustrated with a real-case study in an Iranian power plant project, and the associated results are compared with two well-known decision-making methods under a fuzzy environment. © 2012 Growing Science Ltd. All rights reserved.

Plant location selection has invariably a significant impact on the performance of many companies or manufacturing systems. In this paper, a new integrated methodology is structured to solve this selection problem. Two well-known decision making methods, namely analytic hierarchical process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), are combined in order to make the best use of information available, either implicitly or explicitly. In addition, the Delphi method is utilized to select the most influential criteria by a few experts. The aim of using the AHP is to give the weights of the selected criteria. Finally, the VIKOR method is taken into account to rank potential alternatives. Finally, an application example demonstrates the suitability of the proposed methodology.

The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured. © 2009 WASET.ORG.

In this study, supply chain demand is forecasted with different methods and their results are compared. In this research traditional time series forecasting methods including moving average, exponential smoothing, exponential smoothing with trend at the first stage and finally two machine learning techniques including Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), are used to forecast the long-term demand of supply chain. By using the data set of the component supplier of the biggest Iranian's car company this research is then implemented. The comparison reveals that the results producing by machine learning techniques are more accurate and much closer to the actual data in contrast with traditional forecasting methods. © 2009 Asian Network for Scientific Information.