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A novel project portfolio selection framework towards organizational resilience: Robust Ordinal Priority Approach.
Mahmoudi, Amin; Abbasi, Mehdi; Deng, Xiaopeng.
Afiliación
  • Mahmoudi A; Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, China.
  • Abbasi M; Department of Industrial Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
  • Deng X; Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, China.
Expert Syst Appl ; 188: 116067, 2022 Feb.
Article en En | MEDLINE | ID: mdl-36818824
ABSTRACT
The COVID-19 pandemic has affected the world's economic condition significantly, and construction projects have faced many challenges and disruptions as well. This should be an alarm bell for project-oriented organizations to be prepared for such events and take necessary actions at the earliest time. In this regard, project-oriented organizations should establish their business based on the resilience concept, making them flexible in dealing with risks and decreasing the recovery time after disruptions. The current study proposes a practical conceptual framework for project-oriented organizations to select the most appropriate portfolio based on organizational resilience strategy. First, portfolios are identified, and the projects are clustered based on organizational resilience strategy using the Elbow and Fuzzy C-Means methods. The projects' scores are then determined employing the stakeholders' opinions and Robust Ordinal Priority Approach (OPA-R), which can handle the uncertainty of the input data. After that, each portfolio's score is determined using the obtained scores of the projects, and the best portfolio linked to the organizational resilience strategy is selected. The application of the proposed method to a project-oriented organization is examined, and its usage for the managers of project-oriented organizations is discussed in detail.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Expert Syst Appl Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Expert Syst Appl Año: 2022 Tipo del documento: Article País de afiliación: China