用机器学习优化锯木厂选址，提升供应链效率与可持续性。
Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems
- 融合机器学习与地理分析，构建数据驱动的选址评估框架
- 随机森林表现最优，10-11%的密西西比州土地适合建厂
- 揭示供需比为最关键因素，适合林业规划与政策制定者
战略性地确定锯木厂位置对提升木材供应链的效率、盈利能力和可持续性至关重要。本研究提出一种基于学习的多准则决策（LB-MCDM）框架，将机器学习（ML）与基于地理信息系统（GIS）的空间选址分析通过多准则决策（MCDM）相结合。该框架提供了一种数据驱动、无偏见且可复现的场地适宜性评估方法。我们在密西西比州（MS）开展案例研究，应用五种机器学习算法（随机森林分类器、支持向量分类器、XGBoost分类器、逻辑回归和K近邻分类器）识别最合适的锯木厂位置。其中，随机森林分类器表现最佳。我们采用SHAP（SHapley Additive exPlanations）技术确定各准则的相对重要性，发现反映本地市场竞争动态的供需比是影响最大的因素，其次是道路、铁路线及城市区域距离。验证结果显示，本模型生成的适宜性地图表明，约10%-11%的密西西比州景观高度适合锯木厂布局。
原文摘要 · Abstract (English)
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.
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