Search

Search Results

• MOORA Model Based on New Fuzzy Entropy and Its Application in Recruitment Information System

  Nguyen Huu Hai, Nguyen Van Hanh, Vu Thi Thu Giang, Do Thi Hue, Nguyen Thi Bich Thuy

  Date Received: 20-07-2020 / Date Accepted: 14-04-2021

   Abstract PDF (VIETNAMESE)

  Human resource selection is considered a vital factor for an organization or an enterprise, aiming to select the best candidates in order to meet the job requirements as well as the development of an organization or an enterprise in the future. Personnel selection is Multi-Criteria Decision-Making (MCDM) problem with many conflicting attributes. In this paper, we proposed a new fuzzy entropy measure to improve the calculation of weight of criteria in MOORA method. In addition, the proposed measure has also provided more options to calculate entropy measures of fuzzy sets. Finally, we applied the proposed model to the personnel selection for a training program and choosing the right accountant for a business. The obtained results are similar to those of other methods such as SWARA and FMCDM.

• Evaluating the Potential of Digital Soil Mapping Method to Map Soil Typesin BacNinh Province

  Doan Thanh Thuy, Le Thi Giang

  Date Received: 08-01-2016 / Date Accepted: 29-04-2016

   Abstract PDF (VIETNAMESE)

  The multinomial logistic regression (MLR) model has been being widely used for soil mapping because it could help save time and costs for collecting and analyzing soil data points compared to conventional methods. This research aimed to assess the potential of mapping soil types in Bac Ninh by assessing the performance of Multinomial Logistic Regression (MLR) model in predicting soil types. Nine predictive variables were derived from the ancillary data including land use, altitude, slope, NDVI, PVI, RVI, Topographic Wetness Index and SAGA Wetness Index. MLR model was constructed to predict soil classes at 2 levels: WRB-Reference Soil Group and intermediate level of Soil Group between Reference Soil Group and the full WRB soil name. The map quality was evaluated by the soil map purity estimated with an independent validation dataset. The diversity indices were calculated to assess the information content of the resultant maps. Assessment of the model’s prediction was based on the soil map purity, the Shannon’s entropy and a combined index. MLR yielded high map purity at the level of Reference Soil Group. When the taxonomic level changed from Reference Soil Group level to intermediate level, the map purity decreased while the value of the diversity indices increased. Therefore, soil mapping using MLR in predicting Reference Soil Group might be more efficient. However, at intermediate level, the model predicted higher diversity of soil map and thus the informative value estimated by the combined index was higher.

• Comparison Analysis of some MCDM Modelsand Applying in Decision Information System

  Nguyen Thi Lan, Ngoc Minh Chau, Nguyen Xuan Thao

  Date Received: 20-07-2020 / Date Accepted: 30-11-2020

   Abstract PDF (VIETNAMESE)

  Practically, a decision-maker (DM) often faces the problem of choosing the best optimal from the alternatives based on given criteria, which is called the multiple criteria decision-making problem (MCDM). Many models have been studied and used to solve MCDM problems. However, it is wrong to declare that there is a certain method that is the best one. The main objective of this studywas to analyze and evaluate some methods of solving multi-criteria decision-making problems such as the SAW (Simple Additive Weighting), and MOORA (multi-objective optimization based on ratio analysis) method, and apply them to solve some problems in agriculture. To do that, we analyzed the SAW and MOORA methods and proposed improvement directions in the data normalization step and used fuzzy entropy to calculate weights for the model's criteria. After that, the improved models used to solve some problems in agriculture, such as the problem of choosing a mushroom cultivation formula, the problem of choosing a fertilizer for cashew trees. The results obtained after improvement were considered to be better.

• Application of MOORA and COPRAS Models to Select Materials for Mushroom Cultivation

  Tran Trung Hieu, Nguyen Xuan Thao, Phan Trong Tien, Le Thi Minh Thuy

  Date Received: 16-05-2019 / Date Accepted: 11-07-2019 / Date Published: 26-07-2025

   Abstract PDF (VIETNAMESE)

  Both the mushroom yield and quality are affected by the type of raw materials, the use of additives, and their mixing ratio. Therefore, a proper selection of raw materials and their mixture formulation is of interest in mushroom cultivation to ensure high yield and quality. In our work, COPRAS (complex rate assessment) and MOORA (multi-objective optimization based on ratio analysis)  were applied as models to select the optimal mixture formulation and materials.  The entropy information was used  for measuring the weights of various criteria in the selection process to come up with the best setup. The model was tested with various cases and compared against available state-of-the-art experimental results from other works. The results of our model were proved to be consistent with the results of other experiments.

  DOI: https://doi.org/10.31817/tckhnnvn.2019.17.4.