Academic Journal
Development of a decision support methodology for optimizing ROI in project management
| Title: | Development of a decision support methodology for optimizing ROI in project management |
|---|---|
| Authors: | Alish Nazarov |
| Source: | Technology audit and production reserves; Vol. 2 No. 2(82) (2025): Information and control systems; 58-65 Technology audit and production reserves; Том 2 № 2(82) (2025): Інформаційно-керуючі системи; 58-65 |
| Publisher Information: | Private Company Technology Center, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | project management, decision analysis, оптимізація ROI, Fuzzy TOPSIS, ROI optimization, управління проєктами, Fuzzy AHP, аналіз рішень |
| Description: | The object of this research is the decision-making process in project management aimed at increasing efficiency and optimizing return on investment (ROI). One of the most problematic areas identified during the audit is the limited capability of traditional multi-criteria decision-making (MCDM) methods – such as multi-objective optimization on the basis of ratio analysis (MOORA) and weighted aggregated sum product assessment (WASPAS) – to operate effectively under uncertainty, incorporate qualitative expert judgments, ensure objectivity in calculations, and maintain ranking stability when criteria weights change or when new alternatives and external factors are introduced – conditions often present in real-world management scenarios. To address these limitations, the study employs an integrated fuzzy decision-making model that combines the fuzzy analytic hierarchy process (Fuzzy AHP) and the fuzzy technique for order preference by similarity to ideal solution (Fuzzy TOPSIS). Fuzzy AHP is used to determine the weights of criteria through expert pairwise comparisons, incorporating linguistic assessments transformed into triangular fuzzy numbers. Fuzzy TOPSIS ranks project alternatives by measuring their closeness to the ideal solution under uncertain conditions. The proposed methodology also includes sensitivity analysis and rank reversal testing to validate the model’s robustness. The results demonstrate a stable ranking of three project alternatives, with Alternative B achieving the highest closeness coefficient (0.6628), indicating its superior investment attractiveness. This decision support model integrates expert knowledge, fuzzy logic, and mathematical modeling, and is adaptable to changes in data, incomplete information, and varying evaluation criteria. Compared to classical MCDM approaches, it offers improved accuracy, flexibility, and robustness for strategic decision-making in dynamic environments. |
| Document Type: | Article |
| File Description: | application/pdf |
| ISSN: | 2706-5448 2664-9969 |
| DOI: | 10.15587/2706-5448.2025.326385 |
| Access URL: | https://journals.uran.ua/tarp/article/view/326385 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....e7ed9ec89a2a655b6ae96572b2c79939 |
| Database: | OpenAIRE |
| ISSN: | 27065448 26649969 |
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| DOI: | 10.15587/2706-5448.2025.326385 |