Production scheduling under demand uncertainty in the presence of feedback: Model comparisons, insights, and paradoxes
Production scheduling under demand uncertainty in the presence of feedback: Model comparisons, insights, and paradoxes
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DOI：
10.1016/j.compchemeng.2022.108028
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发表时间：
2022-11-02
影响因子：
4.3
通讯作者：
Maravelias, Christos T.
中科院分区：
文献类型：
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作者：
Avadiappan, Venkatachalam;Gupta, Dhruv;Maravelias, Christos T.
我们研究了在存在反馈的情况下，生产调度中先验不确定性的重要性。首先，我们研究了不同的优化模型（确定性、鲁棒性和随机规划），用于生成开环调度并描述每种情况下的不确定性建模。其次，我们提出了一个进行闭环仿真的正式程序，以便研究和比较不同模型之间的闭环性能，如需求不确定性观察水平、订单大小最大-平均相对差和过程网络上的负载等属性的变化。最后，我们对模拟结果进行了分析，以了解上述属性如何影响跨网络的不同模型的闭环性能，并阐述了观察到的悖论。
We investigate the importance of accounting for uncertainty a priori in production scheduling in the presence of feedback. First, we examine different optimization models (deterministic, robust, and stochastic programming), used to generate the open-loop schedules and describe the modeling of uncertainty in each case. Second, we present a formal procedure for carrying out closed-loop simulations in order to study and compare the closed-loop performance across the models as attributes such as the demand uncertainty observation horizon, order size max-mean relative difference, and load on the process network are varied. Finally, we analyze the results of the simulations to draw insights on how the above attributes affect the closed-loop performance of the different models across networks and expound on the paradoxes observed.