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Framework for Studying Online Production Scheduling under Endogenous Uncertainty
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2019-12-10 , DOI: 10.1016/j.compchemeng.2019.106670
Dhruv Gupta , Christos T. Maravelias

We propose a framework for studying online production scheduling in the presence of endogenous uncertainties. We address uncertainties in (i) processing times; (ii) batch yields; and (iii) unit operating status. First, we illustrate how uncertainty can result in infeasibilities in the incumbent schedule and propose a model for systematic schedule adjustment to restore feasibility in the absence of new scheduling inputs. In this model, we define variables to track and penalize changes between the new and old schedule. Second, we discuss the different probability distributions for the three uncertainties that we consider in this work and how the parameters for these distributions change with sampling frequency. Third, we present a formal procedure for carrying out closed-loop simulations and evaluating closed-loop performance in the presence of these uncertainties. Finally, using this framework we draw useful insights for the design of online scheduling algorithm in the presence of the above three uncertainties.



中文翻译:

内生不确定性下的在线生产计划研究框架

我们提出了一种在存在内生不确定性的情况下研究在线生产计划的框架。我们解决(i)处理时间中的不确定性;(ii)分批产量;(iii)机组的运行状态。首先,我们说明不确定性如何导致现有计划中的不可行性,并提出一种系统的计划调整模型,以在没有新的计划输入的情况下恢复可行性。在此模型中,我们定义变量以跟踪和惩罚新时间表和旧时间表之间的变化。其次,我们讨论了我们在这项工作中考虑的三个不确定性的不同概率分布,以及这些分布的参数如何随采样频率而变化。第三,我们提出了一种在存在这些不确定性的情况下进行闭环仿真和评估闭环性能的正式程序。最后,在存在上述三个不确定性的情况下,我们使用该框架为在线调度算法的设计得出了有益的见解。

更新日期:2019-12-11
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