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A novel method based on clustering and decision-making for construction project portfolio selection.

Mohammad Khalilzadeh1, Peyman Taebi2, Ali Heidari3

  • 1CENTRUM Católica Graduate Business School, Pontificia Universidad Católica del Perú, Lima, Peru.

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This study introduces an integrated method for project portfolio selection in construction companies, using clustering and multi-criteria decision-making to rank and select optimal projects efficiently.

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Area of Science:

  • Construction Management
  • Operations Research

Background:

  • Project portfolio selection is crucial for construction organizations but faces challenges due to complexity and resource limitations.
  • Effective selection methods are needed to optimize project outcomes and resource allocation.

Purpose of the Study:

  • To propose a novel integrated method for project portfolio selection in project-based construction companies.
  • To enhance decision-making by combining clustering and multi-criteria evaluation techniques.

Main Methods:

  • The K-means algorithm for project clustering.
  • The SWARA (Stepwise Weight Assessment Ratio Analysis) method for criteria prioritization.
  • The MULTIMOORA (Multi-Objective Optimization by Ratio Analysis) method for project ranking and selection.
  • Comparison with the WASPAS (Weighted Aggregated Sum Product Assessment) method for verification.

Main Results:

  • 25 construction projects were clustered into 4 groups and ranked using the integrated method.
  • The proposed method successfully clustered and ranked project portfolios based on 5 main criteria and 18 sub-criteria.
  • Rankings derived from sub-criteria were found to be more preferable due to their detailed consideration of desirability.

Conclusions:

  • The integrated K-means, SWARA, and MULTIMOORA method provides an effective approach for project portfolio selection in construction.
  • Utilizing detailed sub-criteria in multi-criteria decision-making enhances the accuracy and preference of project portfolio rankings.