Journal of Transport and Land Use

Learning-based multi-criteria decision-making model for sawmill location problems

Mahid Ahmed

University of Southern Mississippi

Ali K. Dogru

University of Southern Mississippi

Chaoyang Zhang

University of Southern Mississippi

Chao Meng

University of Southern Mississippi

DOI: https://doi.org/10.5198/jtlu.2026.2836

Keywords: Multi-criteria decision-making, Machine learning, GIS-based spatial location analysis, Facility location

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Abstract

Strategically locating a sawmill is vital for enhancing the efficiency, profitability, and sustainability of timber supply chains. Our study proposes a Learning-Based Multi-Criteria Decision-Making (LB-MCDM) framework that integrates machine learning (ML) with GIS-based spatial location analysis via MCDM. The proposed framework provides a data-driven, unbiased, and replicable approach to assessing site suitability. We demonstrate the utility of the proposed model through a case study in Mississippi (MS). We apply five ML algorithms (Random Forest Classifier, Support Vector Classifier, XGBoost Classifier, Logistic Regression, and K-Nearest Neighbors Classifier) to identify the most suitable sawmill locations in MS. Among these models, the Random Forest Classifier achieves the highest performance. We use the SHAP (SHapley Additive exPlanations) technique to determine the relative importance of each criterion, revealing the Supply-Demand Ratio, a composite feature that reflects local market competition dynamics, as the most influential factor, followed by Road, Rail Line and Urban Area Distance. The validation of suitability maps generated by our LB-MCDM model suggests that 10%-11% of the MS landscape is highly suitable for sawmill location.

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