A data-driven multistage adaptive robust optimization framework for planning and scheduling under uncertainty
A data-driven multistage adaptive robust optimization framework for planning and scheduling under uncertainty
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DOI：
10.1002/aic.15792
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发表时间：
2017-10-01
期刊：
影响因子：
3.7
通讯作者：
You, Fengqi
中科院分区：
文献类型：
--
作者：
Ning, Chao;You, Fengqi
提出了一种基于多阶段自适应鲁棒优化（ARO）和非参数核密度M-估计的数据驱动的不确定性优化方法。与传统的鲁棒优化方法不同，该框架引入了分布信息以避免过度保守。采用基于Hampel损失函数的鲁棒核密度估计，通过核化迭代加权最小二乘算法从不确定性数据中提取概率分布。提出了一种数据驱动的不确定性集，其中不确定性参数的边界由分位数函数定义，将多级ARO框架与不确定性数据有机地结合起来。基于这个不确定性集，我们进一步开发了一个精确的强大的对应解决由此产生的数据驱动的多级ARO问题的一般形式。为了说明所提出的框架的适用性，在过程操作中的两个典型的应用：第一个是对过程网络的战略规划，另一个是对多用途批处理过程的短期调度。该方法返回高23.9%的净现值和31.5%的利润比传统的鲁棒优化方法在规划和调度应用，分别。(c)2017美国化学工程师学会AIChE J，63：4343-4369，2017
A novel data-driven approach for optimization under uncertainty based on multistage adaptive robust optimization (ARO) and nonparametric kernel density M-estimation is proposed. Different from conventional robust optimization methods, the proposed framework incorporates distributional information to avoid over-conservatism. Robust kernel density estimation with Hampel loss function is employed to extract probability distributions from uncertainty data via a kernelized iteratively reweighted least squares algorithm. A data-driven uncertainty set is proposed, where bounds of uncertain parameters are defined by quantile functions, to organically integrate the multistage ARO framework with uncertainty data. Based on this uncertainty set, we further develop an exact robust counterpart in its general form for solving the resulting data-driven multistage ARO problem. To illustrate the applicability of the proposed framework, two typical applications in process operations are presented: The first one is on strategic planning of process networks, and the other one on short-term scheduling of multipurpose batch processes. The proposed approach returns 23.9% higher net present value and 31.5% more profits than the conventional robust optimization method in planning and scheduling applications, respectively. (c) 2017 American Institute of Chemical Engineers AIChE J, 63: 4343-4369, 2017