‘MEAN+R’: implementing a web-based, multi-participant decision support system using the prevalent MEAN architecture with R based on a revised intuitionistic-fuzzy multiple attribute decision-making model

This study proposes the novel idea of implementing a decision support system (DSS) based on both the prevalent MEAN architecture and the R statistical language platform. A ‘MEAN+R’ framework is defined, and the effectiveness of this framework is verified by taking it as the basis for the implementation of a real user-side, web-based, multi-participant DSS supporting a group decision-making process of quasi-senior people, pertaining to the selection of the best and commonly-agreed senior center for co-living after retirement. The ‘model base’ of the DSS is a recent MADM model evaluating the criteria-wise source opinions of the decision makers (DMs) toward alternatives in terms of intuitionistic fuzzy numbers (IFNs), but within the model the original multi-objective decision-making (MODM) phase to determine whether the criteria weight vector (CWV) has been replaced with another well-accepted MADM model, which is the AHP. This ensures not only unified grounds for modelling, but also a coherent implementation basis for the MADM model in R. The outside Node.js programs access the ‘data base’ of the DSS managed using MongoDB and will invoke the R-written ‘model base’ codes while necessary. Then, the R program also interacts with the ‘data base’ to exchange the information relevant to the decision from/to users. An illustrative numerical group decision example supported by this DSS is provided, and an efficiency analysis is made by listing the time spent on both data and model bases separately and clearly, while excluding the other processes of the DSS. A key finding is that the computational time can be controlled when incorporating the vector-based R platform, i.e., it leaps forward to the next level only when the size of the problem increases to a certain extent. Finally, since the realized DSS is intended to face end users of the healthcare system (rather than the business-oriented service providers, e.g., hospitals, physicians, nurses, etc.), it may provide some supplement to healthcare decision support (rather than solving other types of problem, such as clinical decisions).