An Adaptive-Network-Based Fuzzy Inference System for Project Evaluation
In this article, a set of key management indicators related to performance of execution, planning, costs, effectiveness, human resources, data quality, and logistics, are considered for the evaluation of a project. Several automated tools support project managers in this task. However, these tools a...
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| Veröffentlicht in: | Ingeniería y universidad 2015-12, Vol.19 (2), p.299-313 |
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| creator | Bermudez Peña, Anié Lugo García, José Alejandro Piñero Pérez, Pedro Yobanis |
| description | In this article, a set of key management indicators related to performance of execution, planning, costs, effectiveness, human resources, data quality, and logistics, are considered for the evaluation of a project. Several automated tools support project managers in this task. However, these tools are still insufficient to accurately assess projects in organizations with continuous improvement management styles and with presence of uncertainty in the primary data. An alternative solution is the introduction of soft computing techniques, allowing gains in robustness, efficiency, and adaptability in these tools. This paper presents an adaptive-network-based fuzzy inference system (ANFIS) to optimize projects evaluation made with the Xedro-GESPRO tool (manufacturer: Universidad de las Ciencias informáticas, [20], versión: 14.05, Cuba). The implementation of the system allowed the adjustment of fuzzy sets parameters in the inference rules for the assessment of projects, based on the automatic calculation of indicators. The contribution of this research lies in the application of ANFIS soft computing technique to optimize the evaluation of projects integrated with the management tool. The results contribute to the improvement of existing decision-making support tools into organizations towards project-oriented production. |
| doi_str_mv | 10.1114/javeriana.iyu19-2.sdib |
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Several automated tools support project managers in this task. However, these tools are still insufficient to accurately assess projects in organizations with continuous improvement management styles and with presence of uncertainty in the primary data. An alternative solution is the introduction of soft computing techniques, allowing gains in robustness, efficiency, and adaptability in these tools. This paper presents an adaptive-network-based fuzzy inference system (ANFIS) to optimize projects evaluation made with the Xedro-GESPRO tool (manufacturer: Universidad de las Ciencias informáticas, [20], versión: 14.05, Cuba). The implementation of the system allowed the adjustment of fuzzy sets parameters in the inference rules for the assessment of projects, based on the automatic calculation of indicators. The contribution of this research lies in the application of ANFIS soft computing technique to optimize the evaluation of projects integrated with the management tool. The results contribute to the improvement of existing decision-making support tools into organizations towards project-oriented production.</description><identifier>ISSN: 0123-2126</identifier><identifier>EISSN: 2011-2769</identifier><identifier>DOI: 10.1114/javeriana.iyu19-2.sdib</identifier><language>eng ; por</language><publisher>Bogotá: Editorial Pontificia Universidad Javeriana</publisher><subject>Adaptive systems ; ANFIS ; Artificial intelligence ; Automation ; Computer engineering ; Continuous improvement ; Cybernetics ; decision ; Decision making ; Efficiency ; ENGINEERING, MULTIDISCIPLINARY ; evaluación de proyectos ; fuzzy inference system ; Fuzzy logic ; Fuzzy sets ; Fuzzy systems ; Human resources ; Indicators ; Inference ; Logistics ; making ; Management styles ; Neural networks ; Organizations ; Project evaluation ; Project management ; Resource management ; Robustness (mathematics) ; sistema de inferencia borroso ; Soft computing ; Software engineering ; toma de decisiones</subject><ispartof>Ingeniería y universidad, 2015-12, Vol.19 (2), p.299-313</ispartof><rights>2015. This work is licensed under (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>This work is licensed under a Creative Commons Attribution 4.0 International License.</rights><rights>LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. 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More info: https://dialnet.unirioja.es/info/derechosOAI</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,870,881,27901,27902</link.rule.ids></links><search><creatorcontrib>Bermudez Peña, Anié</creatorcontrib><creatorcontrib>Lugo García, José Alejandro</creatorcontrib><creatorcontrib>Piñero Pérez, Pedro Yobanis</creatorcontrib><title>An Adaptive-Network-Based Fuzzy Inference System for Project Evaluation</title><title>Ingeniería y universidad</title><addtitle>Ing. Univ</addtitle><description>In this article, a set of key management indicators related to performance of execution, planning, costs, effectiveness, human resources, data quality, and logistics, are considered for the evaluation of a project. 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| subjects | Adaptive systems ANFIS Artificial intelligence Automation Computer engineering Continuous improvement Cybernetics decision Decision making Efficiency ENGINEERING, MULTIDISCIPLINARY evaluación de proyectos fuzzy inference system Fuzzy logic Fuzzy sets Fuzzy systems Human resources Indicators Inference Logistics making Management styles Neural networks Organizations Project evaluation Project management Resource management Robustness (mathematics) sistema de inferencia borroso Soft computing Software engineering toma de decisiones |
| title | An Adaptive-Network-Based Fuzzy Inference System for Project Evaluation |
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