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

• 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; Chemical engineering; column-and-constraint generation algorithm; 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
• DOI: 10.1002/aic.15255

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