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Data-driven distributionally robust optimization of shale gas supply chains under uncertainty

在无常下面的页岩气体供应链的 Datadriven 分布地柔韧的优化

作     者:Gao, Jiyao Ning, Chao You, Fengqi 

作者机构:Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA 

出 版 物:《AICHE JOURNAL》 (美国化学工程师协会志)

年 卷 期:2019年第65卷第3期

页      面:947-963页

核心收录:

学科分类:0817[工学-化学工程与技术] 08[工学] 

基  金:National Science Foundation (NSF) CAREER Award [CBET-1643244] 

主  题:data-driven distributionally robust optimization shale gas supply chain uncertainty mixed-integer linear programming 

摘      要:This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second-stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small-scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. (c) 2018 American Institute of Chemical Engineers AIChE J, 65: 947-963, 2019

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