The maintenance of a sustainable mechanism to curb the rate of overfishing in similar water zones is one of the major challenges facing artisan fisheries in small island regions. Several factors instigate the decision by local fishermen to potentially choose a location to explore. Obviously, the demand to address these triggers, particularly those with negative impact tendencies, has become paramount. This study, therefore, is aimed at mapping out fishing heat spots based on arrival, background of the fishermen, and available species. Subsequently, an evaluation is conducted to ascertain the criteria influencing the decision on a specific position and also to determine the consequences of their negative impact on marine resource sustainability and coastal community welfare. For these reasons, data from fishermen, sellers and local distributors were collected by using questionnaires, and analyzed to generate useful information on related fishing ground. Furthermore, experts were involved in providing expert assessment for Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL). The results showed significant insights and therefore offer support to help regulators preserve marine resources, especially by updating their decision making capacity for increased coastal community welfare. The direct implication of this research also serves as a basis for conducting traditional fisheries, estimated not only to meet economic impact, but also promote sustainable environment.

This study aims at investigating the relationship between the degree of internationalization and firm performance in Cameroon. Based on a panel dataset of 114 firms surveyed by the World Bank from 2009 to 2019, the regression results find evidence that there exists a relationship between internationalization and performance with respect to the W-shape while the model controls the characteristics of the business environment and firm characteristics. This result has confirmed the impact of internationalization on the firm performance going through 4 stages (W-shaped). We draw a number of implications for the study of internationalization in both theory and practice.

Bin packing problem is a constrained optimization problem with a huge search space due to large combinations. Bin packing problem has a wide range of applications in multiple fields. This paper presents harmony search algorithm with different initialization and adaptive PAR strategies for solving bin packing problem. The proposed Harmony search (HS) variations tests two partial feasible initialization strategies for bin packing problem. The paper presents adaptive PAR strategies for better exploration and exploitation of HS algorithm. The PAR values are tuned in every iteration. Improved initialization strategy, population initialization after premature convergence and adaptive PAR leads to the better exploration of harmony search algorithm for bin packing problem. The performance of variations are tested over 120 benchmark instances with 100 and 200 objects with varying complexities. The results show that improved HS performs better than basic HS with respect to best, mean, convergence rate. The performance of algorithms is tested with varying harmony memory size and harmony memory considering rate. Results show that variation in these two parameter values has less effect on performance of improved versions.

One of the most important solutions for dimensionality reduction in data preprocessing, and improving classification performance is gene selection in microarray data since they usually have several thousand genes with very few samples. Because of these characteristics, the complexity of classification models increases and their efficiency decreases. The gene selection problem inherently pursues two goals: reducing the number of genes and increasing the classification efficiency. Therefore, this paper presents a novel hybrid filter-wrapper solution based on the Fisher-score method and Multi-Objective Forest Optimization Algorithm (MOFOA). In the proposed method, as a preprocessing step, the Fisher-score method selects 500 discriminative genes by removing redundant/irrelevant genes. Then, MOFOA searches to find the subset of optimal genes using concepts such as repository, crowding-distance, and binary tournament selection. Moreover, the proposed method solves the gene selection problem and, at the same time, optimizes the kernel parameters in the SVM classification model. Six microarray datasets were used to evaluate the performance of the proposed method. Afterward, a comparison was made between its results and those of the four multi-objective hybrid methods presented in the literature in terms of classification performance, the number of selected genes, running time, and hypervolume criteria. According to the results, in addition to selecting fewer genes, the proposed solution has achieved greater classification accuracy in most cases and has been able to obtain a performance similar to or better than that of other multi-objective gene selection approaches.

This paper considered a case of supplier selection problem in a glove manufacturer located at Yogyakarta, Indonesia that uses genuine sheep leather as the raw material. The problem is solved using both Analytical Hierarchy Process (AHP) and Fuzzy AHP, in which three versions of Fuzzy AHP are applied i.e. Extent Analysis proposed by Chang (1996) [Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655.], Extent Analysis proposed by Wang (2008) [Wang, Y. M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research,186(2), 735-747.], and the modified Fuzzy LLSM proposed by Wang (2006) [Wang, Y. M., Elhag, T. M., & Hua, Z. (2006). A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process. Fuzzy Sets and Systems, 157(23), 3055-3071.]. Moreover, the research is conducted by incorporated four expert respondents, who have more than 12 years of experience in the problem. It is found that the top four priorities obtained from AHP are similar with those from Fuzzy AHP with Extent Analysis proposed by Chang (1996) and Fuzzy AHP with the modified Fuzzy LLSM proposed by Wang (2006). This priority list of supplier can be used by the manufacturer to select the raw material supplier.

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.