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首页> 外文期刊>Uncertain Supply Chain Management >Robust simulation-optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence
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Robust simulation-optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence

机译：使用计算智能在不确定性下的动态随机生产/库存控制系统的鲁棒仿真优化

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摘要

In production/inventory control systems, the goal of the controller is to generate sophisticated decisions by controlling the order rate and inventory level. This paper aims at modeling a dynamic-stochastic production/inventory control system under two sources of variability (uncertainty) including uncertainties on demand rate and frustrating rate. The study deals with obtaining a robust optimal design of a Proportional-Integral-Derivative (PID) controller in in the stochastic control system. For this purpose, a new robust simulation-optimization method in the class of computational intelligence is proposed. To cope with the unknown distribution of uncertainty, the crossing weighted uncertainty scenarios are combined with the proposed method. Within this study, a new sequential robust efficient global optimization is proposed to make a trade-off between optimal and robustness terms in final optimization results. Finally, a numerical case with simulation experiments is conducted to demonstrate the advantages of the proposed policy in terms of optimal result, robustness, and computational cost.
机译：在生产/库存控制系统中，控制器的目标是通过控制订单率和库存级别来生成复杂的决策。本文旨在在两个可变性源（不确定性）下建模动态随机生产/库存控制系统，包括按需率和令人沮丧的率的不确定性。该研究涉及在随机控制系统中获得比例积分（PID）控制器的稳健最佳设计。为此目的，提出了一种在计算智能类中的新的稳健仿真优化方法。为了应对未知的不确定性分布，交叉加权不确定性情景与所提出的方法相结合。在本研究中，提出了一种新的顺序稳健的高效全局优化，以在最终优化结果中进行最佳和鲁棒性术语之间进行权衡。最后，进行了一种具有仿真实验的数值案例，以证明在最佳结果，稳健性和计算成本方面的提出的政策的优势。

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