scholarly journals Optimal design and planning of biodiesel supply chain network: a scenario-based robust optimization approach

2019 ◽  
Vol 11 (1) ◽  
pp. 111-128 ◽  
Author(s):  
M. Rezaei ◽  
S. K. Chaharsooghi ◽  
A. Husseinzadeh Kashan ◽  
R. Babazadeh
Keyword(s):  
Supply Chain ◽  
Optimal Design ◽  
Robust Optimization ◽  
Optimization Approach ◽  
Supply Chain Network

scholarly journals Supply Chain Network Design under Demand Uncertainty and Supply Disruptions: A Distributionally Robust Optimization Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Ruozhen Qiu ◽  
Yizhi Wang
Keyword(s):  
Supply Chain ◽  
Robust Optimization ◽  
Demand Uncertainty ◽  
Optimization Approach ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Supply Disruptions ◽  
Second Order Cone ◽  
Uncertainty Sets ◽  
Distributionally Robust

We develop a robust optimization model for designing a three-echelon supply chain network that consists of manufacturers, distribution centers, and retailers under both demand uncertainty and supply disruptions. The market demands are assumed to be random variables with known distribution and the supply disruptions caused by some of the facilities faults or connection links interruptions are formulated by several scenarios with unknown occurrence probabilities. In particular, we assume the probabilities that the disruption scenarios happen belong to the two predefined uncertainty sets, named box and ellipsoid uncertainty sets, respectively. Through mathematical deductions, the proposed robust SCN design models can be transformed into the tractable linear program for box uncertainty and into second-order cone program for ellipsoid uncertainty. We further offer propositions with proof to show the equivalence of the transformed problems with the original ones. The applications of the proposed models together with solution approaches are investigated in a real case to design a tea supply chain network and validate their effectiveness. Numerical results obtained from model implementation and sensitivity analysis arrive at important practical insights.

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