A fuzzy multi-agent model of project portfolio scheduling and selection taking into account environmental resilience

In the context of increasing complexity and uncertainty in project management, this research introduces a Fuzzy Multi-Agent Model for Project Portfolio Scheduling and Selection that emphasizes environmental resilience. The model leverages fuzzy logic to address the inherent vagueness in project evaluation criteria, allowing for a more nuanced assessment of potential projects. By employing a multi-agent system, the framework facilitates decentralized decision-making, enabling diverse stakeholders to collaborate effectively in the selection and scheduling processes. The study highlights the importance of integrating environmental considerations into project portfolio management, recognizing that sustainable practices are essential for long-term organizational success. The model was analyzed using a multi-objective genetic algorithm, and the results suggested that the model could be solved in various dimensions and that with the increase in dimensions, the values ​​of the objective functions increased as well. The model was then analyzed, and the results of the sensitivity analysis indicated that the parameter of pollution level had the greatest effect on environmental issues and resilience, followed by static waste production, then active waste production, and finally saleable waste. The findings will serve as a guide for practitioners and researchers alike, paving the way for more adaptive and sustainable project portfolio management strategies in the face of uncertainty and change.

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Publication Details

Journal
Results in Control and Optimization
Published
2025-03-14
DOI
https://doi.org/10.1016/j.rico.2025.100544
Primary Topic
Construction Project Management and Performance
Type
article
Field-Weighted Citation Impact
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article

A fuzzy multi-agent model of project portfolio scheduling and selection taking into account environmental resilience

Amir Azizi, Majid Sabzehparvar, Hadis Gholami, Davood Jafari
1 links
Results in Control and Optimization
Construction Project Management and Performance
article

A fuzzy multi-agent model of project portfolio scheduling and selection taking into account environmental resilience

Amir Azizi, Majid Sabzehparvar, Hadis Gholami, Davood Jafari
article en

Abstract

In the context of increasing complexity and uncertainty in project management, this research introduces a Fuzzy Multi-Agent Model for Project Portfolio Scheduling and Selection that emphasizes environmental resilience. The model leverages fuzzy logic to address the inherent vagueness in project evaluation criteria, allowing for a more nuanced assessment of potential projects. By employing a multi-agent system, the framework facilitates decentralized decision-making, enabling diverse stakeholders to collaborate effectively in the selection and scheduling processes. The study highlights the importance of integrating environmental considerations into project portfolio management, recognizing that sustainable practices are essential for long-term organizational success. The model was analyzed using a multi-objective genetic algorithm, and the results suggested that the model could be solved in various dimensions and that with the increase in dimensions, the values ​​of the objective functions increased as well. The model was then analyzed, and the results of the sensitivity analysis indicated that the parameter of pollution level had the greatest effect on environmental issues and resilience, followed by static waste production, then active waste production, and finally saleable waste. The findings will serve as a guide for practitioners and researchers alike, paving the way for more adaptive and sustainable project portfolio management strategies in the face of uncertainty and change.

Results in Control and OptimizationVol. 19
Islamic Azad University, Science and Research Branch (IR), Islamic Azad University, Karaj (IR), Islamic Azad University of Parand (IR)
Climate action
Openalex Percentile: Top 95%
Construction Project Management and Performance
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