Sistema neuro-borroso de apoyo al control de la ejecución de proyectos

During execution control to their projects, organizations employ dissimilar tools to assist specialistsin decision -making. In order to achieve a comprehensive evaluation of the project, a set of keyindicators (time, cost, quality, logistics and human resource performance) are examined by applyingsoft computing techniques using fuzzy inference systems. However, sometimes the fuzzy inferencerules that evaluate indicators are constructed from the judgment of experts, which introducesimprecision and vagueness in the boundaries of linguistic concepts. In this paper, a neuro-fuzzysystem for projects execution control support which optimizes the existing rules base efficiently andeffectively is proposed. The potential benefits of the proposal are related with decision-makingimprovement in organizations to projects-oriented production.

[1]  P. Latha,et al.  Estimating Development Effort of Software Projects using ANFIS , 2012 .

[2]  F. T. Dweiri,et al.  Using fuzzy decision making for the evaluation of the project management internal efficiency , 2006, Decis. Support Syst..

[3]  Malay Bhatt,et al.  Project Classification Using Soft Computing , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[4]  Giovanna Castellano,et al.  Knowledge discovery by a neuro-fuzzy modeling framework , 2005, Fuzzy Sets Syst..

[5]  Antonella Certa,et al.  A Synthetic Measure for the Assessment of the Project Performance , 2010 .

[6]  Witold Pedrycz,et al.  Genetically optimized logic models , 2005, Fuzzy Sets Syst..

[7]  José Alejandro Lugo García,et al.  Colección automática de métricas hacia un repositorio de mediciones , 2011 .

[8]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Hua Gao.  A fuzzy-ANP approach to project management performance evaluation indices system , 2010, 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM).

[10]  P. Taticchi.  Business Performance Measurement and Management , 2010 .

[11]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[12]  Sankar K. Pal,et al.  Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation , 2003, IEEE Trans. Knowl. Data Eng..

[13]  Pablo Moscato,et al.  Evolutionary Computation: Challenges and duties , 2004 .