用机器学习自动选锯木厂位置，比专家经验更准
Learning-Based Multi-Criteria Decision Model for Site Selection Problems
- 结合机器学习与多准则决策，从数据中自动学权重和关键特征
- 在密西西比州案例中显著提升选址准确性，避免人为偏见
- 适合需要客观选址的林业、物流、基建等行业
战略性地确定锯木厂位置对木材供应链的效率、盈利性和可持续性至关重要，但涉及多种因素的复杂决策，如靠近资源与市场、道路和铁路距离、城市区域距离、坡度、劳动力市场及现有锯木厂数据。尽管传统多准则决策（MCDM）方法考虑这些因素，但依赖专家主观赋权易引入偏差。机器学习（ML）模型提供了一种基于数据的客观替代方案，可直接从大规模数据中学习权重，无需主观判断。此外，ML模型能自主识别关键特征，无需人工筛选。本研究提出集成机器学习与MCDM的方法，并通过密西西比州的案例展示该模型在优化锯木厂选址中的有效性。该模型灵活通用，适用于多个行业的选址问题。
原文摘要 · Abstract (English)
Strategically locating sawmills is critical for the efficiency, profitability, and sustainability of timber supply chains, yet it involves a series of complex decision-making affected by various factors, such as proximity to resources and markets, proximity to roads and rail lines, distance from the urban area, slope, labor market, and existing sawmill data. Although conventional Multi-Criteria Decision-Making (MCDM) approaches utilize these factors while locating facilities, they are susceptible to bias since they rely heavily on expert opinions to determine the relative factor weights. Machine learning (ML) models provide an objective, data-driven alternative for site selection that derives these weights directly from the patterns in large datasets without requiring subjective weighting. Additionally, ML models autonomously identify critical features, eliminating the need for subjective feature selection. In this study, we propose integrated ML and MCDM methods and showcase the utility of this integrated model to improve sawmill location decisions via a case study in Mississippi. This integrated model is flexible and applicable to site selection problems across various industries.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。