A Self-Adjusting Decision Support System for Project Portfolio Prioritization using Integrated AHP and Reinforcement Learning
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866902072315609088 |
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| author | Akbari, Masome Akbarpour Shirazi, Mohsen |
| author_facet | Akbari, Masome Akbarpour Shirazi, Mohsen |
| contents | <p>This is the NetLogo Agent-Based Model (ABM) source code used for the research study: 'A Reinforcement Learning Framework for Adaptive Decision-Making in Project Portfolios'.</p> <p><strong>Purpose and Methodology:</strong> The model implements an adaptive decision-making framework for optimizing strategic project prioritization and achieving Project Portfolio Management (PPM) resilience under persistent environmental uncertainty. The framework employs a hybrid approach integrating the Analytical Hierarchy Process (AHP) for establishing Key Performance Indicator (KPI) weightings and a Q-learning algorithm (governed by an ε-greedy policy) for sequential portfolio selection.</p> <p><strong>Findings & Application:</strong> The simulation, demonstrated within this code, shows that the RL-based approach significantly improves the strategic alignment and operational adaptability of the project portfolio compared to traditional methods. The model provides a novel mechanism to connect short-term project actions to long-term strategic organizational objectives.</p> <p>This code serves as the primary software data for the research article and can be used by researchers for replication, validation, and future development of RL-AHP integrated systems in complex organizational contexts.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17396323 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Self-Adjusting Decision Support System for Project Portfolio Prioritization using Integrated AHP and Reinforcement Learning Akbari, Masome Akbarpour Shirazi, Mohsen Project Portfolio Management Reinforcement Learning NetLogo Agent-Based Modeling Analytical Hierarchy Process Strategic Alignment Enviromental Uncertainty Q-Learning Simulation <p>This is the NetLogo Agent-Based Model (ABM) source code used for the research study: 'A Reinforcement Learning Framework for Adaptive Decision-Making in Project Portfolios'.</p> <p><strong>Purpose and Methodology:</strong> The model implements an adaptive decision-making framework for optimizing strategic project prioritization and achieving Project Portfolio Management (PPM) resilience under persistent environmental uncertainty. The framework employs a hybrid approach integrating the Analytical Hierarchy Process (AHP) for establishing Key Performance Indicator (KPI) weightings and a Q-learning algorithm (governed by an ε-greedy policy) for sequential portfolio selection.</p> <p><strong>Findings & Application:</strong> The simulation, demonstrated within this code, shows that the RL-based approach significantly improves the strategic alignment and operational adaptability of the project portfolio compared to traditional methods. The model provides a novel mechanism to connect short-term project actions to long-term strategic organizational objectives.</p> <p>This code serves as the primary software data for the research article and can be used by researchers for replication, validation, and future development of RL-AHP integrated systems in complex organizational contexts.</p> |
| title | A Self-Adjusting Decision Support System for Project Portfolio Prioritization using Integrated AHP and Reinforcement Learning |
| topic | Project Portfolio Management Reinforcement Learning NetLogo Agent-Based Modeling Analytical Hierarchy Process Strategic Alignment Enviromental Uncertainty Q-Learning Simulation |
| url | https://doi.org/10.5281/zenodo.17396323 |