A mixed-data evaluation in group TOPSIS with differentiated decision power

[[abstract]]This main objective of this paper is to provide decision support for mixed data in group Technique for Order Preference by Similarity to Idea Solution (TOPSIS) with differentiated decision power. We use a signum function to compare the ordinal performance of alternatives on any qualitative criterion, or the partial information provided by decision makers. The proposed process for ordinal information is uniformly coherent with the traditional TOPSIS steps, preserving the characteristic of distance-based utilities. Ordinal weights are also considered herein, and the decision power of the group members is formulated by their weights under an agreement in the group. Two examples demonstrate that the proposed approach has some benefits and achieves robustness with two types of sensitivity analyses. Some discussions and their limitations to the approach are also provided.[[notice]]補正完

Insight of the Fuzzy Grey Autoregressive Model

[[abstract]]In our previous research, we proposed a fuzzy grey regression model for solving limited time series data. The present paper follows the previous research and proposes a fuzzy grey autoregressive model for considering that the current value is correlated with previous values. The proposed model combines the advantages of the grey system model, the fuzzy regression model and the autoregressive model. Two illustrated examples are provided in which the amount of internet subscribers in Taiwan and the global demand of LCD TVs are forecasted. The results of these practical applications show that the proposed model can be used to obtain smaller forecasting errors of MAPE and RMSE, and that it makes good forecasts for the next demand period of internet subscribers and LCD TV. Furthermore, this model makes it possible for decision makers to forecast the best and the worst estimates based on fewer observations.[[notice]]補正完畢[[incitationindex]]SC

Forecasting the innovation potential under uncertainty

© Springer Nature Switzerland AG 2019.The nations are looking for ways to increase the capacity and potential of innovation at national and international level. In order for the nations to use the competitive advantages resulting from innovation practices, a country should predict its innovation potential and hence prepare its strategic plans accordingly. The traditional forecasting methods are usually insufficient where sudden and unexpected changes happen nationwide and/or worldwide and limited information is available. The aim of this study is to provide a forecasting approach to predict the future innovation potential. To forecast the innovation potential, the percentage of enterprises with innovative activities is used as the main indicator. In the case of predicting the Turkey’s innovation potential, there exist a few bi-yearly historical data where traditional forecasting methods are insufficient. Therefore, grey forecasting approach that can handle uncertain environments is used in this study. The results indicate that the grey forecasting approach achieved satisfactory results while constructing the grey model with a small sample. In the innovation potential of Turkey, the predicted percentage for organization and/or marketing innovator is found to be highest with 60% where the actual is approximately 51%, and the predicted percentage of enterprises with abandoned/suspended innovation is found to be lowest with 6.5% where the actual is 8%. These predictions of innovation potential can be used to evaluate the effects of national and international policies within the country. Moreover, according to these predictions, the national policies should be improved to enhance the country’s competitive advantage in terms of innovativeness