ON THE PROPERTIES OF ∊-SENSITIVITY ANALYSIS FOR LINEAR PROGRAMMING

Chan-Kyoo Park (), Woo-Je Kim () and Soondal Park ()
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Chan-Kyoo Park: Department of Management, Dongguk University, 3-26 Pil-dong, Jung-gu, Seoul 100-715, Korea
Woo-Je Kim: Department of Industrial and Information Systems Engineering, Seoul National University of Technology, Seoul, 139-743, Korea
Soondal Park: Department of Industrial Engineering, Seoul National University, Seoul, 151-742, Korea

Asia-Pacific Journal of Operational Research (APJOR), 2005, vol. 22, issue 02, 135-151

Abstract: ∊-Sensitivity analysis (∊-SA) is a kind of method to perform sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. In this paper, we discuss the property of ∊-SA analysis and its relationship with other sensitivity analysis methods. First, we present a new property of ∊-SA, from which we derive a simplified formula for finding the characteristic region of ∊-SA. Next, based on the simplified formula, we show that the characteristic region of ∊-SA includes the characteristic region of Yildirim and Todd's method. Finally, we show that the characteristic region of ∊-SA asymptotically becomes a subset of the characteristic region of sensitivity analysis using optimal partition. Our results imply that ∊-SA can be used as a practical heuristic method for approximating the characteristic region of sensitivity analysis using optimal partition.

Keywords: Linear programming; sensitivity analysis; interior-point method; optimal partition (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1142/S0217595905000467

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