Chapter ten Possible applications of sensitivity analysis and linear parametric programming

2011 ◽  
Vol 181-182 ◽  
pp. 577-582
Author(s):  
Jin Zhu ◽  
Xiu Mei Zhang ◽  
Wei Kang

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.


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

Author(s):  
Cheng-fu Chen

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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