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Machine learning assisted multi-criteria decision-making approaches for site selection: A systematic review.
Disha Upadhyay1, Anuj Kumar2, Sangeeta Pant3
1Centre for Interdisciplinary Studies and Research, D Y Patil International University (DYPIU), Akurdi, Pune, India.
Methodsx
|July 1, 2026
Summary
Selecting the right business location is vital for growth. This review shows hybrid decision-making models combining multiple-criteria decision analysis (MCDA) and machine learning (ML) offer superior transparency and predictive power for site selection.
Area of Science:
- Operations Research
- Business Analytics
- Management Science
Background:
- Site selection is a critical strategic decision impacting business sustainability and competitiveness.
- Location choice influences long-term performance, cost efficiency, and operational success.
- Systematic review methodology is essential for synthesizing complex decision-making research.
Purpose of the Study:
- To systematically review and analyze methods used in business site-selection problems.
- To trace the evolution of decision-making methodologies in location analysis.
- To identify challenges and propose future research directions in site selection.
Main Methods:
- Systematic literature review following PRISMA guidelines.
- Application of the Context-Intervention-Mechanism-Outcome (CIMO) framework.
- Analysis of 189 peer-reviewed studies published between 2015 and 2025.
Main Results:
- A significant trend from classical Multi-Criteria Decision Making (MCDM) models to hybrid MCDM+Machine Learning (ML) frameworks was observed.
- Hybrid frameworks effectively combine the transparency of MCDM with the predictive capabilities of ML.
- The review provides the first cross-domain taxonomy of methodological evolution in site selection.
Conclusions:
- Hybrid MCDM+ML frameworks offer a balanced approach, enhancing transparency, predictive accuracy, and stakeholder trust.
- These adaptable frameworks can be applied across various domains with minimal modifications.
- Establishing benchmarks, standards, and validation protocols is crucial for advancing site-selection research and practice.
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