A Novel Multiplicative Model of Multi Criteria Analysis for Robot Selection

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