Developing a Two-Stage Stochastic Programming Model for CO2 Disposal Planning under Uncertainty

2012 ◽  
Vol 51 (8) ◽  
pp. 3368-3380 ◽  
Author(s):  
Jee-Hoon Han ◽  
Jun-Hyung Ryu ◽  
In-Beum Lee
Keyword(s):  
Stochastic Programming ◽  
Programming Model ◽  
Planning Under Uncertainty ◽  
Two Stage ◽  
Stochastic Programming Model

An interval-fuzzy two-stage stochastic programming method for filter management of hydraulic systems

2014 ◽  
Vol 229 (15) ◽  
pp. 2788-2809
Author(s):  
Hui Ji ◽  
Songlin Nie ◽  
Yeqing Huang
Keyword(s):  
Stochastic Programming ◽  
Hydraulic System ◽  
Programming Model ◽  
Optimal Strategies ◽  
Management Policy ◽  
Continuous Variables ◽  
Hydraulic Systems ◽  
Two Stage ◽  
Interval Numbers ◽  
Stochastic Programming Model

An interval-fuzzy two-stage stochastic programming model for filter management of hydraulic system under uncertainties is proposed in this paper. The interval-fuzzy two-stage stochastic programming model integrates the two-stage stochastic programming, fuzzy programming, and interval parameter nonlinear programming into an optimization model of contamination control in hydraulic system. For a typical hydraulic system, it can be used for expressing the uncertainties existed in the purchase cost of filters, contamination ingression and generation rates, and contamination-holding capacity as probability functions, interval numbers, and fuzzy sets. The developed method is applied to examining the decisions on the adoption of bypass filter and selection of filters within multi-segments, multi-period, and multi-option context. All potential scenarios of filters management policy associated with different economic penalties, objectives, and reliability of system are analyzed. The results of the illustrative example show that the reasonable solutions are generated, including binary and continuous variables which help the decision maker identify optimal strategies for filter allocation and selection, planning the adoption of bypass filter under different working conditions.

A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network

2018 ◽  
Vol 195 ◽  
pp. 27-44 ◽  
Author(s):  
Md Abdul Quddus ◽  
Sudipta Chowdhury ◽  
Mohammad Marufuzzaman ◽  
Fei Yu ◽  
Linkan Bian
Keyword(s):  
Supply Chain ◽  
Stochastic Programming ◽  
Programming Model ◽  
Supply Chain Network ◽  
Two Stage ◽  
Fuel Supply ◽  
Stochastic Programming Model

Polynomial Approximation for Two Stage Stochastic Programming with Separable Objective

2012 ◽  
pp. 322-333
Author(s):  
Lijian Chen ◽  
Dustin J. Banet
Keyword(s):  
Monte Carlo ◽  
Stochastic Programming ◽  
Monte Carlo Simulations ◽  
Programming Model ◽  
Optimal Solution ◽  
Two Stage ◽  
Convex Objective Function ◽  
Stochastic Programming Model ◽  
Gradient Type ◽  
Norm Approximation

In this paper, the authors solve the two stage stochastic programming with separable objective by obtaining convex polynomial approximations to the convex objective function with an arbitrary accuracy. Our proposed method will be valid for realistic applications, for example, the convex objective can be either non-differentiable or only accessible by Monte Carlo simulations. The resulting polynomial is constructed by Bernstein polynomial and norm approximation models. At a given accuracy, the necessary degree of the polynomial and the replications are properly determined. Afterward, the authors applied the first gradient type algorithms on the new stochastic programming model with the polynomial objective, resulting in the optimal solution being attained.

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