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Articles by J. Shahrabi
Total Records ( 4 ) for J. Shahrabi
  M. Pariazar , J. Shahrabi , M.S. Zaeri and Sh. Parhizi
  In this research, a methodology was proposed to select optimum maintenance strategy. The study suggested a methodology which applied various steps. This research considered developing a list of criteria with the recognition of patterns amongst those criteria. Having developed the hierarchy structure, then the paper illustrates the use of an AHP improved by Rough set to eliminate the inconsistency commonly existing in the AHP method. A case study is used to demonstrate the application of the various steps of the proposed methodology.
  J. Shahrabi and R. Pelot
  In this study, fishing vessel activities and incidents that occurred within Canadian Atlantic Waters are mapped and examined using Kernel density technique that is used as the advance hot spot technique. This technique precisely identifies location, spatial extent and intensity of incidents hot spots. Recent studies have shown that geospatial information is of fundamental importance to maritime risk analysis providing efficient risk management and geo-information systems represent a powerful new technology that can address many information needs of risk managers and decision makers working with geographically referenced data. This study used the increased capabilities offered by Geomatics techniques and geographic information system to identify hazardous locations for maritime traffic in Canadian Atlantic waters. This research uses spatial analysis to examine risks associated with maritime commercial fishing vessels activities and incidents. The objective of this study is to investigate incident hot spots and ultimately to identify hazardous regions by using a density-based hot spot identification technique. This study examines activities and incidents associated with fishing vessel traffic in the waters of the four Canadian Coast Guard (CCG) SAR statistical areas in the Atlantic region including Cape Breton, Bay of Fundy, SouthWestern Shore and Eastern Shore. In this study the statistical advantage of Kernel density technique was shown. Since the Kernel density method generalizes incidents for the entire study area it also gives a better indication regarding hot spot areas. As a result the value of density estimates at any specific location is developed. The results of this study can help the Coast Guard to deploy resources in order to maximize response capability specifically in these hazardous locations. These methods are also appropriate for finding local concentrations of fishing incidents and the probability of fishing incidents relative to fishing activity.
  J. Shahrabi , S. S. Mousavi and M. Heydar
  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.
  Sh. Parhizi , J. Shahrabi and M. Pariazar
  In this study some data mining techniques for accident investigation and risk analysis is proposed. Function of most of accident investigation and risk analysis methodologies have been based on establishment of different scenarios of accident occurrence and simulation of accidents situation and so far no fundamental action for the analysis of remained data from accident has taken place. This study with the approach of data analysis and using different techniques of data mining can eliminate deficiencies of other techniques therewith covers theirs advantages. In this study factor analysis utilized to identify effective factors on occurrence of accidents. Cluster analysis utilized to classify accidents. A case study in a petrochemical company has been done in order to execute and investigate proposed methodology. The results show four different factors effecting on accident`s occurrence and ten different clusters of accidents recognized. Also association rules exposed to discover all patterns and rules that cause occurrence of accidents.
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