Simulation-based optimization of process control policies for inventory management in supply chains
Simulation-based optimization of process control policies for inventory management in supply chains
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DOI：
10.1016/j.automatica.2006.03.019
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发表时间：
2006-08-01
期刊：
影响因子：
6.4
通讯作者：
Rivera, Daniel E.
中科院分区：
文献类型：
--
作者：
Schwartz, Jay D.;Wang, Wenlin;Rivera, Daniel E.
提出了一种基于仿真的优化框架，包括同时扰动随机逼近（SPSA）作为一种手段，最优地指定内模控制（IMC）和模型预测控制（MPC）为基础的决策政策的供应链库存管理的条件下，涉及供应和需求的不确定性的参数。SPSA技术的有效使用，以提高这类决策算法的性能和功能，并说明了与案例研究，涉及同时优化控制器的调整参数和安全库存水平的供应链网络的灵感来自半导体制造。案例研究的结果表明，安全库存水平可以显着降低，并实现经济效益，同时保持令人满意的供应链运营业绩。(c)2006爱思唯尔有限公司版权所有。
A simulation-based optimization framework involving simultaneous perturbation stochastic approximation (SPSA) is presented as a means for optimally specifying parameters of internal model control (IMC) and model predictive control (MPC)-based decision policies for inventory management in supply chains under conditions involving supply and demand uncertainty. The effective use of the SPSA technique serves to enhance the performance and functionality of this class of decision algorithms and is illustrated with case studies involving the simultaneous optimization of controller tuning parameters and safety stock levels for supply chain networks inspired from semiconductor manufacturing. The results of the case studies demonstrate that safety stock levels can be significantly reduced and financial benefits achieved while maintaining satisfactory operating performance in the supply chain. (c) 2006 Elsevier Ltd. All rights reserved.