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AACS



International Journal of Operations Research and Optimization (IJORO)




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1 INTERACTIVE FUZZY META-GOAL PROGRAMMING FOR PRODUCTION PLANNING IN INDUSTRY

Author: S.R. Singh, A.K. Bhargava and Divya Bansal ,(Pages: 63-82 )

Abstract:

This paper introduced an interactive fuzzy meta-goal programming for production planning in industry. The decision-maker first propose goals toward his/her benefits for the problem, and then sets the target values for the meta-goals, like aggregate achievement, maximum deviation and number of unsatisfied goals assuming these target values being imprecise in nature. Flexibility is one of the most important features of the interactive meta-goal programming. This approach is proposed with the help of an example taken from the production plant. Further, LINDO 15.0 optimizer solver is used to draw results of the problem.

Keyword:

Goal Programming, Interactive Meta-Goal Programming, Interactive Fuzzy , Meta-Goal Programming, Production Planning.

2 Detecting influential observations using Least Absolute Deviations Regression

Author: B. Ashok and R. Elangovan,(Pages: 83-92)

Abstract:

Robust regression is a form of regression analysis designed to circumvent some limitation of traditional parametric and Non-parametric methods. Robust methods are known as resistant of abnormal values and other violations of model assumptions and appropriate for a broad category of distributions. It is an alternative to lease squares regression when data are contaminated with outliers or influential observations. It can be also used for the purpose of detecting influential observations. Detecting influential observations using Least Absolute Deviations Regression are designed to be not overly affected by violation of assumption by the underlying data generating process. In this paper it is proposed to compare LAD method with the iteratively reweighted least square and ordinarily least square method by detecting influential observations. It is observed that LAD method is more efficient in estimating the parameters in all cases, the distribution of errors follows heavy tailed distributions and in the case of contamination of the data with abnormal values. The LAD regression methods are not only robust but give consistent results in detecting outliers.

Keyword:

Deviations Regression, LAD method, Influential observations

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