Materials selection is a multiple attribute decision making (MADM) problem. A lot of MADM methods are applicable to materials selection, and it may produce considerable differences between the results of materials selection. But it is unknown which MADM method is better. So it is desirable to decide reasonable final result of materials selection in consideration of the individual results from different MADM methods. In this paper, materials selection method combined with different MADM methods is proposed. The method is based on final ranks of alternative materials, where the final ranks are determined from the ranks of the alternative materials using different MADM methods. This method is applied to select optimal magnesium alloy material for automobile wheels. This method may be widely used to select optimal material in engineering practice.

The aim of this paper is to propose the methods to select reasonable normalization method in TOPSIS and decide best optimal material combined with individual results from TOPSIS with some popular normalization methods. In this paper, to evaluate performance of normalization method, entropy-based and variation coefficient-based performance scores are introduced. To decide final result of materials selection combined with individual results from TOPSIS with different normalization methods, final rank index of alternative material is proposed. To verify the effectiveness of the proposed methods, TOPSIS with some popular normalization methods is applied to select optimal tribological coating material. As a result, it is desirable to select the normalization method with highest entropy-based and variation coefficient-based performance scores. In order to select best optimal material using TOPSIS with some popular normalization methods, the method to decide final result of materials selection is proposed by using final indices of alternative materials. The proposed methods may be widely used to solve the materials selection problems in engineering practice.

Technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi attribute decision making (MADM) method and it has been widely used in materials selection. However, the main drawback of the traditional TOPSIS is that it has a rank reversal phenomenon. To overcome this drawback, we propose an improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values by modifying normalization formula and ideal solutions. Moreover, to study the impacts of changing attribute weights on relative closeness values of alternatives, we propose a sensitivity analysis method to attribute weights on the relative closeness values of the alternatives. We applied the proposed method to select best absorbent layer material for thin film solar cells (TFSCs). As a result, copper indium gallium diselinide was selected as the best one and the next cadmium telluride from among five materials. When the alternative is added to or removed from the set of original alternatives, the elements of the normalized decision-matrix, PIS, NIS and the relative closeness values don’t change at all, they are always coincide with the corresponding elements of the original ones. The relative closeness values are absolute values irrelevant to the composition of the alternatives in the improved TOPSIS, while the relative closeness values are relative values relevant to the composition of the alternatives in the traditional TOPSIS. Therefore, the proposed TOPSIS overcomes the rank reversal phenomenon, perfectly. It could be actively applied to practical problems for materials selection.

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