Optimal supply chain design and operations under multi-scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Although strategic and operational uncertainties differ in their significance of impact, a “one‐size‐fits‐all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model th...

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Bibliographic Details
Published in:AIChE journal Vol. 62; no. 9; pp. 3041 - 3055
Main Authors: Yue, Dajun, You, Fengqi
Format: Journal Article
Language:English
Published: New York Blackwell Publishing Ltd 01.09.2016
American Institute of Chemical Engineers
Subjects:
Algorithms
Biofuels
Chemical engineering
column-and-constraint generation algorithm
Design
Design engineering
Mathematical models
Modelling
multi-scale uncertainties
Optimization
Optimization algorithms
stochastic robust optimization model
Stochasticity
supply chain optimization
Supply chains
Uncertainty
USA, Illinois
ISSN:0001-1541, 1547-5905
Online Access:Get full text
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Summary:Although strategic and operational uncertainties differ in their significance of impact, a “one‐size‐fits‐all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi‐scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi‐level mixed‐integer linear programming model is solved by a decomposition‐based column‐and‐constraint generation algorithm. To illustrate the application, a county‐level case study on optimal design and operations of a spatially‐explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the solution algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3041–3055, 2016
Bibliography:Institute for Sustainability and Energy at Northwestern University (ISEN)
ArticleID:AIC15255
ark:/67375/WNG-VSD35HJM-7
National Science Foundation (NSF) - No. CBET-1554424
istex:05E0A280E279B98141519E2EDB003EF0A1A633AB
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.15255