Preliminary normalization is central to the decision process of several popular, recent or completely new multi-attribute decision-making (MADM) methods. However, a number of authors have pointed out serious pitfalls attributed to normalization methods. One major pitfall, which has been identified, is that normalization methods may lead to different final rankings of alternatives when a ranking procedure (RP) based on them is used for solving a MADM problem. The current paper aims to ascertain and illustrate the effectiveness of some RPs based on prominent primary WEighted Self-NORmalizing Distance (WESNORD) metrics and their averages. The effectiveness of the selected RPs is demonstrated by solving a logistics service provider (LSP) selection problem taken from the literature. The results reveal that the RPs considered deliver final rankings of alternatives, which are very similar to the SAW-produced reference ranking.

Selection of robots from the several proposed alternatives is a very important and tedious task. Decision makers are not limited to one method and several methods have been proposed for solving this problem. This study presents Polygons Area Method (PAM) as a multi attribute decision making method for robot selection problem. In this method, the maximum polygons area obtained from the attributes of an alternative robot on the radar chart is introduced as a decision-making criterion. The results of this method are compared with other typical multiple attribute decision-making methods (SAW, WPM, TOPSIS, and VIKOR) by giving two examples. To find similarity in ranking given by different methods, Spearman’s rank correlation coefficients are obtained for different pairs of MADM methods. It was observed that the introduced method is in good agreement with other well-known MADM methods in the robot selection problem.

Tourism industry plays an essential role on development of economy and it is considered as one of green industries. Many countries try to promote investment on tourism to create various job opportunities. This paper presents an empirical investigation to rank different factors influencing on tourism industry in city of Yazd, Iran. The proposed study uses three multiple criteria decision making techniques namely, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW) and Taxonomy to rank 40 factors. The results indicate that factors such as Being located in the center axis of communication, Strengths in transportation, The ease of access to tourism destinations and Sufficient knowledge of some historical attractions are among important factors influencing tourism industry.

The evaluation of the most appropriate flexibility in the manufacturing sector is one of the strategic issues that may affect the Flexibile Manufacturing System (FMS). In this paper, a Multiple Attribute Decision Making Method (MADM) methodology is structured to resolve this problem. The two decision making methods, which are Simple Additive Weighting (SAW) and Weighted Product Method (WPM), are integrated with Analytical hierarchy process (AHP) in order to get the best use of information available. The aim of using AHP is to give the weights of the attributes and these weights are used in SAW & WPM method for ranking of flexibility in FMS. Furthermore, the method uses fuzzy logic to change the qualitative attributes into the quantitative attributes. 15 factors are taken to evaluation of 15 flexibility. In this report, we concluded that Product Flexibility has the most impact in 15 flexibilities and Programme Flexibility has the least impact in these 15 flexibilities by this methodology.