Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems
This addresses the problem of optimizing sawmill placement for timber industry stakeholders, but it is incremental as it applies existing ML methods to a specific domain.
The study tackled the problem of strategically locating sawmills to improve timber supply chain efficiency by proposing a Learning-Based Multi-Criteria Decision-Making framework that integrates machine learning with GIS analysis, achieving identification of 10-11% of Mississippi's landscape as highly suitable for sawmill locations.
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 Mississippi. Among these models, the Random Forest Classifier achieved 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.