The paper proposed an integrated approach that combines Bayesian Networks and Total Cost of Ownership (TCO) to construct the plant location decision model. The goal is to efficiently represent uncertain data and ambiguous information, and to unite them to improve the quality of the decisions. It is a hierarchical Bayesian Network, considering both tangible and intangible location decision criteria, also including factors and cost elements, so that decision problems can be decomposed into many parts which are easy to understand and handle. At the end, an empirical example is used to proved the feasibility and validity of the model%提出运用贝叶斯网络与总体拥有成本(TCO)结合的方法,建立工厂选址决策模型.旨在更有效的表述不确定性数据和模棱两可的信息,起到改进决策质量的作用.首先对贝叶斯网络的基础知识进行介绍,进而阐述了基于贝叶斯网络的工厂选址模型的实施步骤,在分析和评价与选址有关的成本时,将成本要素层细分为不同的因素层和准则层,同时考虑有形和无形的选址决策准则,将决策问题分解成许多易于理解和处理的子部分.最后,实例验证选址模型的可行性和有效性.
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