Facing the issues of structural complexity, on which stakeholders have different views, has increasingly led to the use of Soft Systems Methodology (SSM) in solving managerial problems. Moreover, the weaknesses of this methodology in considering all point of views and ensuring the effectiveness of the proposed changes have provided the motivation for applying Fuzzy Cognitive Map (FCM) in SSM. Using FCM as a modeling tool makes it possible to combine the views of different experts and form group FCM (GFCM). GFCM has the potential to be applied as a useful decision support tool in the stage of offering recommendations and changes. The methodology proposed in this article is applied to ticketing system of Raja passenger train company. This system, influenced by various policies and views, is analyzed with the recommended methodology and then the solutions for developing the system are suggested in a prioritized manner.
Credit scoring is a commonly used method for evaluating the risk involved in granting credits. Both Genetic Programming (GP) and Ant Colony Optimization (ACO) have been investigated in the past as possible tools for credit scoring. This paper reports an investigation into the relative performances of GP, ACO and a new hybrid GP-ACO approach, which relies on the ACO technique to produce the initial populations for the GP technique. Performance of the hybrid approach has been compared with both the GP and ACO approaches using two well-known benchmark data sets. Experimental results demonstrate the dependence of GP and ACO classification accuracies on the input data set. For any given data set, the hybrid approach performs better than the worse of the other two methods. Results also show that use of ACO in the hybrid approach has only a limited impact in improving GP performance.
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