Advances in Sensitivity Analysis and Parametric Programming.

1998 ◽  
Vol 49 (7) ◽  
pp. 770
Author(s):  
J. M. Wilson ◽  
T. Gal ◽  
H. J. Greenberg
Keyword(s):  
Sensitivity Analysis ◽  
Parametric Programming

MILP Sensitivity Analysis for Short-Term Scheduling of Batch Plants under Uncertainty

2011 ◽  
Vol 181-182 ◽  
pp. 577-582
Author(s):  
Jin Zhu ◽  
Xiu Mei Zhang ◽  
Wei Kang
Keyword(s):  
Sensitivity Analysis ◽  
Branch And Bound ◽  
Parametric Programming ◽  
Uncertain Parameters ◽  
Deterministic Case ◽  
Short Term ◽  
Batch Plants ◽  
Bound Solution ◽  
Solution Methodology

In this paper, An integrated framework is developed to handle uncertainty in short-term scheduling based on the idea of inference-based sensitivity analysis for MILP problems and the utilization of a branch and bound solution methodology. The proposed method leads to the determination of the importance of different parameters and the constraints on the objective function and the generation and evaluation of a set of alternative schedules given the variability of the uncertain parameters. The main advantage of the proposed method is that no substantial complexity is added compared with the solution of the deterministic case because the only additional required information is the dual information at the leaf nodes of the branch-and-bound tree. Two case studies are presented to highlight the information extracted by the proposed approach and the complexity involved compared with parametric programming studies.

Batch Process and Sensitivity Analysis of Collision Detection of Planar Convex Polygons in Motion

2013 ◽  
Vol 13 (4) ◽  
Author(s):  
Cheng-fu Chen
Keyword(s):  
Sensitivity Analysis ◽  
Programming Problem ◽  
Collision Detection ◽  
Parametric Programming ◽  
Batch Process ◽  
Detection Problem ◽  
Convex Polygons ◽  
Parametric Problem ◽  
Decomposition Procedure ◽  
Two Parameters

A new method for formulation, solution, and sensitivity analysis of collision detection of convex objects in motion is presented. The collision detection problem is formulated as a parametric programming problem governed by the changes in the relative translation and relative rotation between the two objects considered. The two parameters together determine all the possible relative configurations between two moving convex objects. Therefore, solving this parametric problem allows for knowing the proximity information for all the possible configurations of the objects. We develop a two-step decomposition procedure to solve this parametric programming problem, and show that the solution is a convex function of the two parameters. This convexity feature enables an archive of the proximity information and sensitivity analysis for the collision detection problem.

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