[2604.04996] Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems

Computer Science > Machine Learning arXiv:2604.04996 (cs) [Submitted on 5 Apr 2026] Title:Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems Authors:Mahid Ahmed, Ali Dogru, Chaoyang Zhang, Chao Meng View a PDF of the paper titled Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems, by Mahid Ahmed and 2 other authors View PDF HTML (experimental) 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 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 Ro...