Criticality-Based Management of Facility Assets
Effective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential. This research introduces a comprehensive decision support system, which is a framework organized into three models: the criticality model, the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/14/2/339 |
| _version_ | 1827343936317816832 |
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| author | Alaa Salman |
| author_facet | Alaa Salman |
| author_sort | Alaa Salman |
| collection | DOAJ |
| description | Effective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential. This research introduces a comprehensive decision support system, which is a framework organized into three models: the criticality model, the rehabilitation model, and the optimum criticality model to manage the rehabilitation of facility assets. The criticality model utilizes the Analytical Hierarchy Process (AHP) to assess the group of assets. Emphasizing criticality as a central management factor, this model lays the foundation for subsequent decision-making. The rehabilitation model employs an Artificial Neural Network (ANN), integrating Customer Level of Service (CLoS), Technical Level of Service (TLoS), and asset criticality to determine appropriate rehabilitation actions. <i>NeuralTools 7.5</i> is leveraged for precise predictions of rehabilitation strategies tailored to specific assets. The third model, optimum criticality, focuses on prioritizing rehabilitation activities within the constraints of limited budgets. <i>Lingo 20.0</i> is utilized to optimize rehabilitation activities, considering budget limitations and other constraints, offering a strategic approach to maximize the impact of available resources. This integrated framework provides decision-makers with a systematic and data-driven approach to facility management, enhancing the efficiency and effectiveness of rehabilitation actions. An academic building was chosen as a hypothetical example to implement the three models and suggest the essential considerations for managing both the academic building itself and other infrastructure assets. The results obtained demonstrate that the principles and methodologies encapsulated in this project can be extrapolated and scaled up for application to large-scale infrastructure assets, ensuring the sustenance of the requisite level of service and the management of acceptable risk on a broader scale. |
| first_indexed | 2024-03-07T22:39:44Z |
| format | Article |
| id | doaj.art-6d8fd9463d7043f4a1781ff930fe3245 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-07T22:39:44Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-6d8fd9463d7043f4a1781ff930fe32452024-02-23T15:09:57ZengMDPI AGBuildings2075-53092024-01-0114233910.3390/buildings14020339Criticality-Based Management of Facility AssetsAlaa Salman0College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 34211, Saudi ArabiaEffective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential. This research introduces a comprehensive decision support system, which is a framework organized into three models: the criticality model, the rehabilitation model, and the optimum criticality model to manage the rehabilitation of facility assets. The criticality model utilizes the Analytical Hierarchy Process (AHP) to assess the group of assets. Emphasizing criticality as a central management factor, this model lays the foundation for subsequent decision-making. The rehabilitation model employs an Artificial Neural Network (ANN), integrating Customer Level of Service (CLoS), Technical Level of Service (TLoS), and asset criticality to determine appropriate rehabilitation actions. <i>NeuralTools 7.5</i> is leveraged for precise predictions of rehabilitation strategies tailored to specific assets. The third model, optimum criticality, focuses on prioritizing rehabilitation activities within the constraints of limited budgets. <i>Lingo 20.0</i> is utilized to optimize rehabilitation activities, considering budget limitations and other constraints, offering a strategic approach to maximize the impact of available resources. This integrated framework provides decision-makers with a systematic and data-driven approach to facility management, enhancing the efficiency and effectiveness of rehabilitation actions. An academic building was chosen as a hypothetical example to implement the three models and suggest the essential considerations for managing both the academic building itself and other infrastructure assets. The results obtained demonstrate that the principles and methodologies encapsulated in this project can be extrapolated and scaled up for application to large-scale infrastructure assets, ensuring the sustenance of the requisite level of service and the management of acceptable risk on a broader scale.https://www.mdpi.com/2075-5309/14/2/339facility managementAnalytical Hierarchy Process (AHP)Artificial Neural Network (ANN)rehabilitation methodslevel of service |
| spellingShingle | Alaa Salman Criticality-Based Management of Facility Assets Buildings facility management Analytical Hierarchy Process (AHP) Artificial Neural Network (ANN) rehabilitation methods level of service |
| title | Criticality-Based Management of Facility Assets |
| title_full | Criticality-Based Management of Facility Assets |
| title_fullStr | Criticality-Based Management of Facility Assets |
| title_full_unstemmed | Criticality-Based Management of Facility Assets |
| title_short | Criticality-Based Management of Facility Assets |
| title_sort | criticality based management of facility assets |
| topic | facility management Analytical Hierarchy Process (AHP) Artificial Neural Network (ANN) rehabilitation methods level of service |
| url | https://www.mdpi.com/2075-5309/14/2/339 |
| work_keys_str_mv | AT alaasalman criticalitybasedmanagementoffacilityassets |