The main goal of this study is to investigate the classification capability of several machine learning (ML) techniques, including decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for predicting purchase decisions. The application case is related to consumer purchase decisions of domestic goods in the context of Vietnam. Firstly, factors influencing Vietnamese consumers’ purchase decision of domestic products were identified. Then, data from 240 consumers in Vietnam were collected. Different classifying models based on ML techniques were developed to analyse the sampling data after the performances of the models were evaluated and compared using confusion matrix, accuracy rate and several error indexes. The results indicate that the DT(J48) obtained the highest performance with the corrected prediction percentage of 91.6667%. The findings also show that machine-learning techniques can be used to explicitly in forecasting Vietnamese consumers’ purchase behaviour.

Nowadays, special attention has been paid to the environmental and social issues in both developed and developing countries. Therefore, research on the sustainable supply chain is greatly expanded dramatically over the years. In some industries like food industry, these issues are more significantly emphasized because of the particular characteristics the food products. In this paper, we considered a multi-objective model for designing the sustainable supply chain in the processed food industry with fixed shelf-life products. Model objectives include economic with profit maximization as an economic index, environmental with the index of carbon dioxide emissions and wastewater treatment in manufacturing sector and social objectives with maximizing the amount of jobs created as a social index. For the proposed model, in addition to determining the optimal location of the facility and the flow rate between facility, the type of delivery of products is determined either directly from plant or indirectly from distribution centers with mechanized transportation system. Finally, the model is implemented in a processed food industry as a case study in Iran and is solved via the augmented ε-constraint method.

In the existing market, companies confront a fierce competition, so the need for new and efficient process for supply chain has become necessarily important. To this end, supply chain management among multi agent system is proposed for addressing the selection and evaluation process related to the inbound logistics. However, most of recent systems deal solely with the negotiation including the selection of one or multiple suppliers, without supporting the transportation provider selection simultaneously and consider it as decision criterion that affect the final choice of cooperative suppliers. As part of win-win negotiation, active supplier involvement can enhance efficiency and effectiveness of supply chain. Then again, transport cost constitutes the most important factor in the third of the total operational costs of a supply chain. To face this challenge, a new form of supplier selection including transportation provider selection is proposed. For this purpose, we present a multi-issue decision protocol based on ontology to support the negotiation between upstream nodes of supply chain in the proposed multi agent system. Furthermore, the automated multi-criteria analysis model based on combined analytical hierarchy process (AHP) and TOPSIS is judged helpful for decision-makers to make quick decision with less human interactions.

Evaluating research productivity of lecturer is a vital process of universities for teaching improvement and administrative decision making. Lecturers’ research productivity evaluation is a difficult and sensitive problem which has many objective and subjective criteria, complexity and imprecision. Therefore, the evaluation of lecturers’ research productivity can be viewed as a multi-criteria decision making (MCDM) problem in vague environment. This study develops an integrated MCDM approach for evaluating the lecturers’ research productivity. In the proposed approach, a fuzzy analytic hierarchy process (AHP) is applied to determine the weights of criteria. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method using interval neutrosophic sets is further adopted for showing the preference order of the lecturers’ research productivity in an educational organization. Finally, the proposed approach is applied to solve the lecturers’ research productivity evaluation problem in the case of University of Economics and Business, Vietnam National University, Hanoi (UEB-VNU).

In every nation's Green Growth Strategy, enterprise's green investment plays a vital role for sustainable development. In order to develop green investment activity for sustainable development, it is necessary to identify factors affecting green finance and sustainable development of businesses. This study identifies and measures the factors affecting green investment in Vietnamese using the Exploratory Factor Analysis (EFA) methodology to process the dataset from 208 businesses in different industries in 2018.The results show that most of the factors, which were included in the survey, were reliable, maintained statistical significance, and converged into the group of factors. The factors in the survey includes Infrastructure for green investment, Difficulties in approaching funding for green investment activities, Incentives to access capital for green investment, Understanding of enterprises' green investment, Support from the Government in accessing to fund for green investment, The capital that businesses can access for green investment, The enterprise plans to implement green investment projects actively and the special incentives of green investment. Notably, the group of factors about green investment awareness, awareness on accessing green capital, the role of the Government, and green capital mobilization tools had substantial impacts on the green investment implementation of Vietnamese businesses. On that basis, the proposed recommendations focus on the central role of the Government, the legal framework, the diversification of green financing, and green capital mobilization tools. Raising awareness of businesses in accessing and using these capital raising tools as well as promoting green investment communication are solutions according to the evaluation of more than 200 surveyed enterprises.

This study considers representing decision maker preferences by Choquet integral in existence of interactions among criteria. Parameters of the Choquet integral are capacities which assign weights not only to criteria but also to each subset of criteria. This property provides Choquet integral with the ability of modeling some types of interactions. Different capacity types with different degrees of complexity have been defined in the literature. After making a review on the dependence (interaction) and independence concepts used in the multiple criteria decision making literature, we study and represent structures of interactions that can be handled by different capacity types through intuitive graphical demonstrations. Afterwards, we provide guidelines for specifying the appropriate capacity type in practical applications. Such guidance has not been provided in the literature for the practitioners to the best of our knowledge.

The continuous increase in energy consumption has brought worldwide attention to its significant environmental effect, which is triggered by the increase in greenhouse gas emissions, global warming, and rapid climate change. As such, more energy efficient buildings are required to minimize the energy consumption of heating and cooling. The present study introduces a set of machine learning-based models to predict the heating and cooling loads in buildings. This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines. The comparisons were conducted as per mean absolute percentage error (MAPE), mean absolute error (MAE) and root-mean squared error (RMSE). Finally, the significance of the capacities of the machine learning models are evaluated using two-tailed student’s t-tests. Results demonstrate that the radial basis function network outperformed the aforementioned machine learning models.

A non-dominated sorting genetic algorithm (NSGA-II) is applied to obtain Pareto optimal solutions in widely used advanced machining processes, i.e., electric discharge machining, electrochemical micromachining, ultrasonic machining, abrasive water jet machining. The solutions obtained using the proposed method is in the form of the Pareto-optimal front, thus, any solution is acceptable and can be utilized to obtain optimum performance of the considered processes. The obtained results using NSGA-II show good agreement with the results of previous researchers. Implementation of the proposed method shows benefits to the process engineer of the industries as they can select alternative parameters based on the requirement.

Village tourism is one of the alternative forms of sustainable tourism that began to be developed in Bali. Bongkasa Pertiwi tourism village is one of the villages designated as a tourist village with a variety of natural potential. The purpose of this research is to analyze the strategy and development program of the Bongkasa Pertiwi Tourism Village, and to analyze the factors that play a role in the sustainability of the Bongkasa Pertiwi Tourism Village. The main data collection technique in this study is through FGD, the results of which are input to the SWOT and MICMAC analysis. The results of the SWOT analysis show that Bongkasa Pertiwi Tourism Village has strong category competitiveness, and high category attractiveness as a tourist village, so the strategic position of the development of the Kenderan tourism village is in the condition of growth and build. The three main factors in the sustainability of a tourist village are the uniqueness of the tourist village, the competence of human resources, and the presence of a driving force that has a link with stakeholders.

The paper studies the monetary approach to exchange rate for a group of five Pacific Basin economies, using quarterly data for the period of post – Asian financial crisis. Estimated results reveal that for Thailand and the Indonesia which were most affected by Asian financial crisis, monetary model did not work for explaining exchange rate movements. For Korea and Malaysia, the results show that there were long-run relationships between exchange rates and their monetary variables. However, the proportionality hypothesis of exchange rate to relative money supply did not hold for the two countries. Conversely, for Vietnam, it appears that the monetary model worked well in explaining exchange rate movements. Especially, the estimated coefficients of money and output variables are consistent with any traditional variants of monetary model.