A Novel Multiplicative Model of Multi Criteria Analysis for Robot Selection
B. Bairagi, B. Dey, B. Sarkar, and S. Sanyal. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI),, 1 (3):
9(December 2012)
Abstract
Selection of an industrial robot for a specific purpose is one of the most challenging problems in modern
manufacturing atmosphere. The selection decisions become more multifaceted due to continuous
incorporation of advanced features and facilities as the decision makers in the manufacturing
environment are to asses a wide varieties of alternatives based on a set of conflicting criteria. To assist
the selection procedure various Multiple Criteria Decision Making (MCDM) approaches are available.
The present investigation endeavours to mitigate and unravel the robot selection dilemma employing the
newly proposed Multiplicative Model of Multiple Criteria Analysis (MMMCA) approach. MMMCA is a
novel model in which all performance ratings are converted into numerical values greater than and equal
to unity and converting all non-benefit rating into benefit category. Each normalized weight is used as the
index of corresponding normalized ratings those are multiplied to obtain the resultant score. The best
alternative is associated with the highest resultant score. A real life example is cited in order to
demonstrate and validate the applicability, potentiality, suitability, flexibility and validity of the proposed
model. At last sensitivity analysis is carried out for making dynamic decision
%0 Journal Article
%1 noauthororeditor
%A Bairagi, Bipradas
%A Dey, Balaram
%A Sarkar, Bijan
%A Sanyal, Subir
%D 2012
%J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI),
%K Analysis Criteria Industrial Model Multiple Multiplicative Robot Selection Sensitivity of
%N 3
%P 9
%T A Novel Multiplicative Model of Multi Criteria Analysis for Robot Selection
%U http://airccse.org/journal/ijscai/papers/1212scai01.pdf
%V 1
%X Selection of an industrial robot for a specific purpose is one of the most challenging problems in modern
manufacturing atmosphere. The selection decisions become more multifaceted due to continuous
incorporation of advanced features and facilities as the decision makers in the manufacturing
environment are to asses a wide varieties of alternatives based on a set of conflicting criteria. To assist
the selection procedure various Multiple Criteria Decision Making (MCDM) approaches are available.
The present investigation endeavours to mitigate and unravel the robot selection dilemma employing the
newly proposed Multiplicative Model of Multiple Criteria Analysis (MMMCA) approach. MMMCA is a
novel model in which all performance ratings are converted into numerical values greater than and equal
to unity and converting all non-benefit rating into benefit category. Each normalized weight is used as the
index of corresponding normalized ratings those are multiplied to obtain the resultant score. The best
alternative is associated with the highest resultant score. A real life example is cited in order to
demonstrate and validate the applicability, potentiality, suitability, flexibility and validity of the proposed
model. At last sensitivity analysis is carried out for making dynamic decision
@article{noauthororeditor,
abstract = {Selection of an industrial robot for a specific purpose is one of the most challenging problems in modern
manufacturing atmosphere. The selection decisions become more multifaceted due to continuous
incorporation of advanced features and facilities as the decision makers in the manufacturing
environment are to asses a wide varieties of alternatives based on a set of conflicting criteria. To assist
the selection procedure various Multiple Criteria Decision Making (MCDM) approaches are available.
The present investigation endeavours to mitigate and unravel the robot selection dilemma employing the
newly proposed Multiplicative Model of Multiple Criteria Analysis (MMMCA) approach. MMMCA is a
novel model in which all performance ratings are converted into numerical values greater than and equal
to unity and converting all non-benefit rating into benefit category. Each normalized weight is used as the
index of corresponding normalized ratings those are multiplied to obtain the resultant score. The best
alternative is associated with the highest resultant score. A real life example is cited in order to
demonstrate and validate the applicability, potentiality, suitability, flexibility and validity of the proposed
model. At last sensitivity analysis is carried out for making dynamic decision},
added-at = {2018-08-22T06:52:21.000+0200},
author = {Bairagi, Bipradas and Dey, Balaram and Sarkar, Bijan and Sanyal, Subir},
biburl = {https://www.bibsonomy.org/bibtex/292cb0f90c16b83b3ac8be44c4f26fca1/leninsha},
interhash = {fb6420bd29b7c60562fa4d1b5ab4e25c},
intrahash = {92cb0f90c16b83b3ac8be44c4f26fca1},
issn = {2319 - 1015},
journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI),},
keywords = {Analysis Criteria Industrial Model Multiple Multiplicative Robot Selection Sensitivity of},
month = {December},
number = 3,
pages = 9,
timestamp = {2018-08-22T06:52:21.000+0200},
title = {A Novel Multiplicative Model of Multi Criteria Analysis for Robot Selection },
url = {http://airccse.org/journal/ijscai/papers/1212scai01.pdf},
volume = 1,
year = 2012
}