عنوان مقاله [English]
Decision-making as one of the principles of management is considered an important factor in prosperity of the organizations. This is so important that managers use efficient tools to improve the quality of their decisions. Steel industry is one of the major industries in this country; consequently, it deserves special attention. In this paper, the main aim is to use scientific methods to manage crude steel consumption in the country. However, the literature shows that it is relatively difficult to yield accurate results in the prediction of consumption, especially in long-term horizon. Researchers believe that high level of complexity and uncertainty in financial markets is main reason of this matter. Therefore, in this paper, a hybrid of intelligent and soft computing models have been used as an effective way in order to model the complexities and uncertainties simultaneously in the data. In this way, the list of variables is recognized based on the literature and expert opinions. Then the linear and nonlinear relationships and also correlations between variables are evaluated and final explanatory variables specified. Finally, four models including hard classic, soft classic, hard intelligent and soft intelligent are designed to predict steel consumption in both short and long
term horizons and their results are compared with each other. Empirical results
indicate that using the hard intelligent model makes improvement 22.68% and
41.41% in comparison with hard classic model in short and long term horizons
respectively in Root Mean Squared Error (RMSE). In addition, the soft intelligent model makes improvement 43.01% and 92.72% in comparison with soft classic model and hard classic model respectively in short term horizon and 34.68% and 91.53% in long term horizon. Results of the study indicate superiority of the soft intelligent models over hard intelligent models and superiority of hard intelligent models over hard classic models. Results of the study indicate superiority of the soft intelligent models and hard intelligent models over hard intelligent models and hard classic models respectively.