Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems
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 suitabl...
| Authors | Mahid Ahmed, Ali Dogru, Chaoyang Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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