Multi-Stage Adjustable Robust Optimization for Process Scheduling Under Uncertainty

被引:104
作者
Lappas, Nikolaos H. [1 ]
Gounaris, Chrysanthos E. [1 ]
机构
[1] Carnegie Mellon Univ, Dept Chem Engn, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会;
关键词
process scheduling; uncertainty; robust optimization; MULTIPURPOSE BATCH PLANTS; CONTINUOUS-TIME FORMULATION; MIXED-INTEGER OPTIMIZATION; DEMAND UNCERTAINTY; MATHEMATICAL FORMULATION; STOCHASTIC PROGRAMS; MILP FORMULATION; LINEAR-PROGRAMS; TERM; MODELS;
D O I
10.1002/aic.15183
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Variations in parameters such as processing times, yields, and availability of materials and utilities can have a detrimental effect in the optimality and/or feasibility of an otherwise "optimal" production schedule. In this article, we propose a multi-stage adjustable robust optimization approach to alleviate the risk from such operational uncertainties during scheduling decisions. We derive a novel robust counterpart of a deterministic scheduling model, and we show how to obey the observability and non-anticipativity restrictions that are necessary for the resulting solution policy to be implementable in practice. We also develop decision-dependent uncertainty sets to model the endogenous uncertainty that is inherently present in process scheduling applications. A computational study reveals that, given a chosen level of robustness, adjusting decisions to past parameter realizations leads to significant improvements, both in terms of worst-case objective as well as objective in expectation, compared to the traditional robust scheduling approaches. (C) 2016 American Institute of Chemical Engineers
引用
收藏
页码:1646 / 1667
页数:22
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