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 addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data‐driven adaptive robust optimization approach. A semiempirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then used to construct the uncertainty sets for modeling these uncertain parameters. The data‐driven uncertainty sets are incorporated into a two‐stage adaptive robust mixed‐integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multilevel optimization model is transformed into its robust counterpart, which is a single‐level MILP problem. The proposed framework is applied to the steam system of a real‐world ethylene plant to demonstrate its applicability. © 2018 American Institute of Chemical Engineers
- NSF-PAR ID:
- 10461591
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 65
- Issue:
- 7
- ISSN:
- 0001-1541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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