To addresses the design and operations of resilient supply chains under uncertain disruptions, a general framework is proposed for resilient supply chain optimization, including a quantitative measure of resilience and a holistic biobjective two‐stage adaptive robust fractional programming model with decision‐dependent uncertainty set for simultaneously optimizing both the economic objective and the resilience objective of supply chains. The decision‐dependent uncertainty set ensures that the uncertain parameters (e.g., the remaining production capacities of facilities after disruptions) are dependent on first‐stage decisions, including facility location decisions and production capacity decisions. A data‐driven method is used to construct the uncertainty set to fully extract information from historical data. Moreover, the proposed model takes the time delay between disruptions and recovery into consideration. To tackle the computational challenge of solving the resulting multilevel optimization problem, two solution strategies are proposed. The applicability of the proposed approach is illustrated through applications on a location‐transportation problem and on a spatially‐explicit biofuel supply chain optimization problem. © 2018 American Institute of Chemical Engineers
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. © 2018 American Institute of Chemical Engineers
- NSF-PAR ID:
- 10459728
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 65
- Issue:
- 3
- ISSN:
- 0001-1541
- Page Range / eLocation ID:
- p. 947-963
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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