A Composite Weight based Multiple Attribute Decision Support System for the Selection of Automated uided Vehicles

This paper proposes a decision support system which integrates the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the composite weights of importance of the attributes. Using fuzzy set theory the qualitative attributes are converted into the quantitative attributes. Based on this model, a decision support system AGVSEL is developed for the selection of AGVs. AGVs are ranked by using the technique for order preference by similarity to ideal solution (TOPSIS), block TOPSIS and modified synthetic evaluation method (M-TOPSIS). The effectiveness of the support system is demonstrated with an illustrative example. The computational results obtained enable evaluation and selection of an appropriate AGV. Sensitivity analysis reveals that at a moderate value of interpolating factor rank transition takes place for topmost position thereby achieving better insight into the complex interplay of subjective and objective weights. Finally, the results of the proposed approach are compared with the results obtained by published methods. Thus, the proposed weight method with AGVSEL system improves decision making in MADM environment.