The present research aims to present a new method based on clustering and decision-making for project portfolio selection in project-based companies. The proposed integrated method includes the K-means method for clustering projects, the SWARA method for prioritizing the identified criteria, and the MULTIMOORA method for ranking and selecting the projects of the studied company. In addition, the results of the MULTIMOORA method was compared with the results of the WASPAS method for verification. First, five criteria (including 18 sub-criteria) were selected using literature review and expert judgment for clustering and ranking project portfolios. Then, the research data was collected from a questionnaire containing identified criteria and sub-criteria. Based on the obtained results, 25 available construction projects were placed and ranked in 4 clusters. The findings show that the proposed integrated method was able to cluster the project portfolios and select the best project portfolios by ranking the project portfolios based on the identified criteria and sub-criteria. Also, the findings indicate that the rankings using five main criteria were different from the rankings using 18 sub-criteria, and therefore due to the nature of the sub-criteria and the importance of paying attention to the «desirability or undesirability of the criteria/sub-criteria in using the MULTIMOORA”, the rankings using the sub-criteria were more preferable.
The present research proposes a novel approach that combines K-Means clustering with SWARA and MULTIMOORA techniques to support project portfolio selection. First, the K-Means method is applied to categorize projects based on their characteristics, allowing for a clearer understanding of project groupings and strategic relevance. Subsequently, the SWARA and MULTIMOORA methods are utilized to rank and select the most suitable projects for inclusion in the portfolio, based on multiple criteria that align with organizational goals. The SWARA and MULTIMOORA methodologies are employed for project portfolio selection due to their complementary strengths in handling complex, multi-criteria decision-making problems. SWARA is ideal for determining the relative importance of criteria based on expert judgment, making it suitable for projects where subjective assessments, such as strategic alignment and risk, are crucial (Kahraman et al., 2015; Rashidi et al., 2021). MULTIMOORA, on the other hand, excels in evaluating alternatives based on both quantitative and qualitative criteria, offering a holistic approach to ranking and selecting projects (Zavadskas et al., 2016; Sadiq et al., 2020; Chen et al., 2021; Li et al., 2022). Together, these methods provide a comprehensive framework that addresses both the subjective and objective aspects of project portfolio selection, ensuring alignment with organizational goals while effectively managing resources and risks.
By integrating these advanced methods, this research aims to provide a more comprehensive and adaptable framework for project portfolio selection, particularly in environments characterized by resource constraints, uncertainty, and conflicting objectives.
In summary, the present study contributes to the literature by introducing a novel, integrated methodology for project portfolio selection that combines clustering with advanced MULTIMOORA and SWARA methods. This approach not only enhances decision-making in practice but also offers new avenues for future research in both project management and multi-criteria decision analysis.
The current research was conducted with the aim of providing a new method for selecting proper projects in Pars Garma Construction & Industrial Company which operates in multiple civil engineering sectors, including dam and dike construction, irrigation and drainage execution, road and bridge construction, drilling and tunnel work, heavy concrete and metal constructions, social housing development, and the design and construction of production plants. Considering the wide range of this organization’s activities in numerous fields and the existence of projects with different characteristics, the projects were first classified based on their characteristics and then the projects in each category were ranked according to the desirable criteria in order to select the right portfolio of projects.
The proposed methodology for selecting the project portfolio is presented in Figure 1. According to the process displayed in Figure 1, first the criteria presented in Table 1 were screened and localized. Next, the projects were clustered using the K-means algorithm, then the weight of each criterion was determined using the SWARA method. Subsequently, the projects in each category were ranked using the MULTIMOORA method according to the desired criteria, and eventually the results were compared with the results of the WASPAS method for verification.
The decision-making panel in this research consisted of 23 senior managers and project managers of the company selected based on three key criteria: (1) minimum 10 years of industry experience, (2) direct involvement in project portfolio decisions, and (3) representation across all operational domains (finance, technical, risk management, and strategic planning), and the required data was collected using the three following questionnaires: (1) Questionnaire for screening the project selection criteria, (2) Questionnaire for weighting the criteria using the SWARA method, and (3) Questionnaire for comparing the projects using the MULTIMOORA method based on the criteria. Figure 1 illustrates the steps of the research methodology.
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The objective of the present study was to develop and present a novel methodology for selecting the appropriate project portfolio in project-based organizations. This methodology integrates the K-means clustering algorithm, the SWARA technique, and the MULTIMOORA method to provide a comprehensive and efficient solution for project portfolio selection. While previous studies have focused primarily on individual aspects of project portfolio management, limited attention has been given to the categorization of similar projects. Categorization plays a critical role in improving the efficiency, reliability, and speed of the project selection process, which is one of the key advantages of our approach over other existing methodologies. By grouping similar projects at the outset, decision-makers can streamline the selection process, ultimately saving time and resources. The use of the K-means clustering algorithm in this methodology significantly enhances the efficiency of the process. K-means, known for its speed and scalability, is particularly effective in handling large datasets, making it well-suited for project-based organizations dealing with multiple potential projects. This clustering technique helps organizations evaluate projects quickly by pre-emptively filtering out inefficient or less relevant projects, allowing managers to focus on those that align with strategic goals. The speed of the K-means algorithm is an important factor in project portfolio selection, as it accelerates the decision-making process, which can otherwise be time-consuming in large organizations with numerous projects.
Following the clustering phase, the MULTIMOORA method is employed to rank projects within each cluster based on both qualitative and quantitative criteria. This multi-faceted ranking system enables organizations to evaluate projects from different perspectives, such as cost, strategic fit, and potential impact, simultaneously. By considering these multiple dimensions, the MULTIMOORA method offers a holistic evaluation, allowing for more informed decision-making in selecting projects that align with the organization’s objectives. To support the evaluation and weighting of the criteria, the SWARA method is utilized due to its simplicity, speed, and effectiveness. The SWARA technique provides an intuitive, step-by-step approach to determining the relative importance of each criterion, making it a practical choice for fast-paced decision-making environments. The rapid assessment of criteria weights increases the overall efficiency of the project evaluation process, allowing organizations to prioritize projects more quickly and accurately.
In this study, the proposed methodology was applied to evaluate 25 projects based on 18 sub-criteria. The projects were then classified into four distinct clusters, and the results were statistically validated using an ANOVA test. Following the clustering phase, the projects within each cluster were ranked based on their relative performance. This approach gives managers the flexibility to choose the highest-scoring projects from each cluster or to select entire clusters that align with the organization’s resources and strategic goals. This enables organizations to prioritize and implement projects that best suit their operational capacity and long-term vision.
This study advances project portfolio selection theory by integrating clustering (K-means) with MCDM (SWARA-MULTIMOORA), addressing a critical gap in handling heterogeneous projects. The proposed framework extends prior MCDM models by demonstrating how clustering can enhance decision efficiency without sacrificing robustness, a theoretical bridge between strategic grouping and multi-criteria evaluation.
Despite the strengths of this research, it is important to acknowledge several limitations. One key challenge was the incomplete response from some experts who participated in the study, as well as instances where certain questionnaires were left unfinished. This lack of full participation may have affected the robustness of the data collected. Additionally, the availability of some key managers and experts was constrained, limiting their involvement in the research process. Time constraints also played a role in preventing these stakeholders from fully engaging with the practical applications and proposals derived from the study. As a result, the ability to obtain timely feedback regarding the implementation of the findings within the organization was hindered.
Given these limitations, future research should focus on refining and expanding the methodologies used in this study. Further testing of the proposed approach across different industries would help validate its generalizability and identify sector-specific adjustments that could enhance its applicability. Moreover, integrating additional complementary techniques, such as fuzzy logic or the Analytic Hierarchy Process (AHP), could further enrich the decision-making process by handling uncertainty and subjective assessments more effectively.
Furthermore, future studies could explore the initial evaluation of projects using mathematical methods like Data Envelopment Analysis (DEA), which could provide a more comprehensive performance assessment of each project. In addition, other clustering methods such as hierarchical clustering and density clustering can be utilized and compared with the K-Means clustering method. Also, optimization models tailored to the goals and constraints of the organization should be developed, enabling the selection of projects based on both strategic objectives and available resources. These advancements in Multi-Criteria Decision-Making (MCDM) practices will contribute to a more sophisticated, adaptable, and reliable approach to project portfolio selection, enhancing the long-term effectiveness of project-based organizations.
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