Simulation-based optimization with surrogate models - Application to supply chain management
Simulation-based optimization with surrogate models - Application to supply chain management
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DOI：
10.1016/j.compchemeng.2005.02.018
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发表时间：
2005-05
期刊：
影响因子：
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通讯作者：
Xiaotao Wan;J. Pekny;G. Reklaitis
中科院分区：
文献类型：
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作者：
Xiaotao Wan;J. Pekny;G. Reklaitis
仿真广泛应用于与供应链管理相关的决策过程。在本文中，我们提出了一个扩展的模拟为基础的优化框架，这是以前提出的分析供应链。该扩展包括迭代构建的代理模型的基础上，系统积累的模拟结果捕捉的关键决策变量和供应链绩效之间的因果关系。然后，可以使用替代模型代替单独的模拟运行来优化决策变量，以节省整体计算工作量。该框架采用了最小二乘支持向量机（LSSVM）、贝叶斯证据框架和计算机实验设计与分析（DACE）等技术来实现目标。扩展的框架说明使用两个小的例子，然后应用于优化库存水平的三级供应链。结果表明，该框架能有效地识别出好的解，并能适应机会约束，具有很好的可扩展性。
Simulation is widely used in the decision-making processes associated with supply chain management. In this paper, we present an extension of the simulation-based optimization framework which has been previously proposed for analyzing supply chains. The extension consists of the iterative construction of a surrogate model based on systematically accumulated simulation results to capture the causal relation between the key decision variables and supply chain performance. The decision variables can then be optimized using the surrogate model in place of individual simulation runs to economize on the overall computational effort. Several techniques are embedded in the framework to achieve the targeted objective: least square support vector machine (LSSVM), Bayesian evidence framework, and design and analysis of computer experiment (DACE). The extended framework is illustrated using two small examples and then applied to optimize the inventory levels in a three-stage supply chain. The results show that the framework identifies good solutions efficiently, can accommodate chance constraints and scales up well.