@article{
 title = {Resilient design and operations of process systems: Nonlinear adaptive robust optimization model and algorithm for resilience analysis and enhancement},
 type = {article},
 year = {2017},
 identifiers = {[object Object]},
 keywords = {Mixed-integer fractional programming,Process design and operations,Resilience,Superstructure optimization,Two-stage adaptive robust optimization},
 id = {da681946-cf14-30a7-8a1d-89599fddc5bf},
 created = {2017-12-15T21:38:15.489Z},
 file_attached = {false},
 profile_id = {f27171f8-b200-3870-9028-dcb6a084733d},
 last_modified = {2017-12-15T21:38:15.489Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
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 abstract = {© 2017 Elsevier Ltd. This paper is concerned with the resilient design and operations of process systems in response to disruption events. A general framework for resilience optimization is proposed that incorporates an improved quantitative measure of resilience and a comprehensive set of resilience enhancement strategies for process design and operations. The proposed framework identifies a set of disruptive events for a given system, and then formulates a multiobjective two-stage adaptive robust mixed-integer fractional programming model to optimize the resilience and economic objectives simultaneously. The model accounts for network configuration, equipment capacities, and capital costs in the first stage, and the number of available processes and operating levels in each time period in the second stage. A tailored solution algorithm is developed to tackle the computational challenge of the resulting multi-level optimization problem. The applicability of the proposed framework is illustrated through applications on a chemical process network and a shale gas processing system.},
 bibtype = {article},
 author = {Gong, J. and You, F.},
 journal = {Computers and Chemical Engineering}
}

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This paper is concerned with the resilient design and operations of process systems in response to disruption events. A general framework for resilience optimization is proposed that incorporates an improved quantitative measure of resilience and a comprehensive set of resilience enhancement strategies for process design and operations. The proposed framework identifies a set of disruptive events for a given system, and then formulates a multiobjective two-stage adaptive robust mixed-integer fractional programming model to optimize the resilience and economic objectives simultaneously. The model accounts for network configuration, equipment capacities, and capital costs in the first stage, and the number of available processes and operating levels in each time period in the second stage. A tailored solution algorithm is developed to tackle the computational challenge of the resulting multi-level optimization problem. 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