A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty

被引：71
作者：
Li, Can [1 ]
Grossmann, Ignacio E. [1 ]
机构：
[1] Carnegie Mellon Univ, Dept Chem Engn, Pittsburgh, PA 15213 USA
来源：
FRONTIERS IN CHEMICAL ENGINEERING | 2021年 / 2卷
关键词：
Stochastic programming; process systems engineering; optimization; decision-making under uncertainty; data-driven;
D O I：
10.3389/fceng.2020.622241
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
Uncertainties are widespread in the optimization of process systems, such as uncertainties in process technologies, prices, and customer demands. In this paper, we review the basic concepts and recent advances of a risk-neutral mathematical framework called "stochastic programming" and its applications in solving process systems engineering problems under uncertainty. This review intends to provide both a tutorial for beginners without prior experience and a high-level overview of the current state-of-the-art developments for experts in process systems engineering and stochastic programming. The mathematical formulations and algorithms for two-stage and multistage stochastic programming are reviewed with illustrative examples from process industries. The differences between stochastic programming under exogenous uncertainty and endogenous uncertainties are discussed. The concepts and several data-driven methods for generating scenario trees are also reviewed.
引用
收藏
页数：20
相关论文
共 85 条
  • [1] Acevedo J., Pistikopoulos E.N., Stochastic optimization based algorithms for process synthesis under uncertainty, Comput. Chem. Eng, 22, pp. 647-671, (1998)
  • [2] Apap R.M., Grossmann I.E., Models and computational strategies for multistage stochastic programming under endogenous and exogenous uncertainties, Comput. Chem. Eng, 103, pp. 233-274, (2017)
  • [3] Beale E.M.L., On minimizing a convex function subject to linear inequalities, J. Roy. Stat. Soc. B, 17, pp. 173-184, (1955)
  • [4] Birge J.R., Decomposition and partitioning methods for multistage stochastic linear programs, Oper. Res, 33, pp. 989-1007, (1985)
  • [5] Birge J.R., Louveaux F., Introduction to stochastic programming, (2011)
  • [6] Boland N., Dumitrescu I., Froyland G., Kalinowski T., Minimum cardinality non-anticipativity constraint sets for multistage stochastic programming, Math. Program, 157, pp. 69-93, (2016)
  • [7] Bonami P., Salvagnin D., Tramontani A., Implementing automatic Benders decomposition in a modern MIP solver, International conference on integer programming and combinatorial optimization, pp. 78-90, (2020)
  • [8] Calfa B.A., Agarwal A., Grossmann I.E., Wassick J.M., Data-driven multi-stage scenario tree generation via statistical property and distribution matching, Comput. Chem. Eng, 68, pp. 7-23, (2014)
  • [9] Cao Y., Zavala V.M., A scalable global optimization algorithm for stochastic nonlinear programs, J. Global Optim, 75, pp. 393-416, (2019)
  • [10] Christian B., Cremaschi S., Heuristic solution approaches to the pharmaceutical R&D pipeline management problem, Comput. Chem. Eng, 74, pp. 34-47, (2015)
123456789