A Hybrid Parametric/Stochastic Programming Approach for Mixed-Integer Nonlinear Problems under Uncertainty

2002 ◽  
Vol 41 (1) ◽  
pp. 67-77 ◽  
Author(s):  
Tanya S. Hené, ◽  
Vivek Dua ◽  
Efstratios N. Pistikopoulos
Keyword(s):  
Stochastic Programming ◽  
Nonlinear Problems ◽  
Mixed Integer ◽  
Programming Approach ◽  
Mixed Integer Nonlinear Problems

Global optimization of mixed-integer nonlinear problems

AIChE Journal ◽  
2000 ◽  
Vol 46 (9) ◽  
pp. 1769-1797 ◽  
Author(s):  
C. S. Adjiman ◽  
I. P. Androulakis ◽  
C. A. Floudas
Keyword(s):  
Global Optimization ◽  
Nonlinear Problems ◽  
Mixed Integer ◽  
Mixed Integer Nonlinear Problems

scholarly journals DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems

2020 ◽  
Vol 78 (1) ◽  
pp. 1-36 ◽  
Author(s):  
Burcu Beykal ◽  
Styliani Avraamidou ◽  
Ioannis P. E. Pistikopoulos ◽  
Melis Onel ◽  
Efstratios N. Pistikopoulos
Keyword(s):  
Nonlinear Problems ◽  
Data Driven ◽  
Mixed Integer ◽  
Mixed Integer Nonlinear Problems

scholarly journals Multi-objective optimization of a power-to-hydrogen system for mobility via two-stage stochastic programming

2021 ◽  
Vol 2042 (1) ◽  
pp. 012034
Author(s):  
Marta Fochesato ◽  
Philipp Heer ◽  
John Lygeros
Keyword(s):  
Stochastic Programming ◽  
Large Scale ◽  
Mixed Integer ◽  
Programming Approach ◽  
Total Annual Cost ◽  
Multi Objective Optimization ◽  
Two Stage ◽  
Mixed Integer Linear Program ◽  
Multi Objective ◽  
Design Variables

Abstract A systematic way for the optimal design of renewable-based hydrogen refuelling stations in the presence of uncertainty in the hydrogen demand is presented. A two-stage stochastic programming approach is used to simultaneously minimize the total annual cost and the CO2 footprint due to the electricity generation sources. The first-stage (design) variables correspond to the sizing of the devices, while the second-stage (operation) variables correspond to the scheduling of the installed system that is affected by uncertainties. The demand of a fleet of fuel cell vehicles is synthesized by means of a Poisson distribution and different scenarios are generated by random sampling. We formulate our problem as a large-scale mixed-integer linear program and we rely on a two-level approximation scheme to keep the problem computationally tractable. A solely deterministic setting which does not take into account uncertainties leads to underestimated device sizes, resulting in a significant fraction of demand remaining unserved with a consequent loss in revenue. The multi-objective optimization produces a convex Pareto front, showing that a reduction in carbon footprint comes with increasing costs and thus diminishing profit.

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