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Resilient supply chain design and operations with decision‐dependent uncertainty using a data‐driven robust optimization approach
Author(s) -
Zhao Shipu,
You Fengqi
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16513
Subject(s) - supply chain , resilience (materials science) , robust optimization , mathematical optimization , computer science , set (abstract data type) , stochastic programming , measure (data warehouse) , production (economics) , construct (python library) , supply chain optimization , operations research , supply chain management , engineering , data mining , mathematics , economics , physics , macroeconomics , political science , law , thermodynamics , programming language
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 AIChE J , 65: 1006–1021, 2019