There are so many paper shredder products available in the market, and the decision to select a 'right shredder' usually involves a number of criteria. For organizations, complexity arises when the procurement function is to purchase a massive amount of shredders of the same type. This study initiated the idea to use the analytic hierarchy process with graph theory and matrix approach for solving the problem. The proposed model determines the best shredder from a pool of alternatives, given the buyer-side decision-maker's preference settings. With the expert questionnaire polled and the heterogeneous real data collected, the model is applied to a reduced data set. The size of the decision problem is defined as 8 alternatives are filtered among 26, while the 7 justification attributes considered are fully kept for not losing the experimental meaning. The result shows the effectiveness and applicability of the approach to manage the encountered decision scientifically. As this also implies that not only buyers but also the manufacturers can use this model to analyse 'something', it is confident to conduct more future studies. The R script which implements the dynamic programming concept to calculate the assessed index scores for graph theory and matrix approach is perhaps another contribution of this study.

Abstract:For an R&D institution to design a specific high investment cost product, the budget is usually 'large but limited'. To allocate such budget on the directions with key potential benefits (e.g., core technologies) requires, at first and at least, a priority over the involved design criteria, as to discover the relevant decision knowledge for a suitable budgeting plan. Such a problem becomes crucial when the designed product is relevant to the security and military sustainability of a nation, e.g., a next generation fighter. This study presents a science education framework that helps to obtain such knowledge and close the opinion gaps. It involves several main tutorial phases to construct and confirm the set of design criteria, to establish a decision hierarchy, to assess the preferential structures of the decision makers (DMs) (individually or on a group basis), and to perform some decision analyses that are designed to identify the homogeneity and heterogeneity of the opinions in the decision group. The entire framework has been applied in a training course hold in a large R&D institution, while after learning the staff successfully applied these knowledge discovery processes (for planning the budget for the fighter design works and for closing the opinion gaps present). With the staffs' practical exercises, several empirical findings except for the budgeting priority (e.g., the discrimination between 'more important criteria' against the less important ones) are also interesting. For some examples (but not limited to these), it is found that the results from using two measures (statistical correlation vs. geometrical cosine similarity) to identify the opinion gaps are almost identical. It is found that DMs' considerations under various constructs are sometimes consistent, but often hard to be consistent. It is also found that the two methods (degree of divergence (DoD) vs. number of observed subgroups (NSgs)) that are used to understand the opinions' diversity under the constructs are different. The proposed education framework meets the recent trend of data-driven decision-making, and the teaching materials are also some updates to science education.

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This study determines the effectiveness of intuitionistic-fuzzy multi-attribute decision-making (IF-MADM) for making group decisions in practice. The effectiveness of the method is measured in terms of four dimensions: applicability, efficacy, efficiency and informativeness. To measure the efficacy, an IF-MADM model that has been recently proposed, AHP and the TOPSIS approach, which are compensatory models for group MADM, are used to model and solve the same collective decision. Using non-parametric statistical tests for data analytics, a ‘similarity confirmation method’ is proposed for a pair-wise test. This is to determine whether the score vectors are similar. Score vectors are used to determine the final ordinal ranks and whose scales differ greatly for different MADM methods. Since the latter two MADM models are both trustworthy with a known range of applications, any similarity in the results verifies the efficacy of IF-MADM. Using this process, the applicability of IF-MADM modelling is demonstrated. The efficiency and informativeness are also benchmarked and justified in terms of the model’s ability to produce a more informed decision. These results are of interest to practitioners for the selection and application of MADM models. Finally, the selection of a senior centre, which is a real group decision problem, is used to illustrate these. This extends the empirical application of IF-MADM, as relatively few studies practically compare issues for IF-MADM with those for other MADM models. The study also supports a rarely studied non-clinical healthcare decision that is relevant because there are many aging societies.

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