Method for Project Execution Control based on Soft Computing and Machine Learning
| Autor: | Anie Bermudez Pena, Giselle Lorena Núñez Núñez, Danny Saavedra Cevallos, Jorge Luis Zambrano Santa, Inelda Anabelle Martillo Alcivar, Gilberto Fernando Castro, Diana Maria Lupez Alvarez |
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| Rok vydání: | 2019 |
| Předmět: |
Soft computing
Artificial neural network Computer science business.industry media_common.quotation_subject Management styles Machine learning computer.software_genre Fuzzy logic Adaptability Robustness (computer science) Artificial intelligence Project management business Gradient descent computer media_common |
| Zdroj: | CLEI |
| DOI: | 10.1109/clei47609.2019.235097 |
| Popis: | To support decision-making, organizations employ dissimilar tools during their projects execution control. However, they are still insufficient in environments with uncertain information and changing conditions in management styles. Deficiencies in systems for controlling the projects execution, affects the quality of their classification in aiding decision-making. An alternative solution is the introduction of soft computing techniques, which provide robustness, efficiency and adaptability at tools. This research proposes a method for project execution control based on soft computing and machine learning, which contributes to improve the project management. The proposed method allows the machine learning and adjusting of fuzzy inference systems to the project evaluation. The results are obtained from the execution of seven algorithms, which are based on space partitioning, neural networks, gradient descent and genetic algorithms. Validation of the proposed system, integrated to a project management tool, ratifies an improvement in the quality of project evaluation. The obtained result provides a contribution to the perfection of tools to support the decision-making in project management organization |
| Databáze: | OpenAIRE |
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