Performance assessment of teaching competency plays an important role in educational activities. Previous assessments of lecturers’ performance have failed to distinguish between potential capacity and their teaching effectiveness. To solve this problem, the integrated approach of quantitative assessment and multi-criteria decision-making models has become one of the main trends for assessing the performance of lecturers in multiple dimensions: self, peer-, manager- and student-based evaluation. This paper proposes a novel hierarchical approach, developed by the Technique for Order preference by Similarity to Ideal Solution method in an interval-valued complex neutrosophic set environment, to more accurately and comprehensively understand the evaluation process and fit it into a systematic framework. An application is given to illustrate a practical solution in lecturer’s evaluation. The accuracy of the proposed method is verified by comparing with other methods.

The hybrid vehicle routing problem is an extension of the green vehicle routing problem where vehicles can use different fuels. In this research, a bi-objective hybrid vehicles routing problem is presented and solved bu considering alternative paths, various lengths and reliabilities. The first objective function minimizes overall system costs and fuel consumption and greenhouse gas emissions, whereas the second objective function maximizes the reliability of the entire system with alternative paths and several reliabilites. The proposed model is formulated as mixed-integer nonlinear programming and a bi-objective simulated annealing (MOSA) algorithm along with the e-constraint method is used as solution strategy. The implementation of the proposed method is presented using some numerical instances.

Agricultural food supply chains of different grains such as wheat, rice, and corn include several processes from cropping and harvesting to distribution. Integrating these processes to reduce costs, in addition to providing sufficient supply, are of the major goals of agri-food supply chain management. Rice is an important grain that constitutes an important part of people’s diets throughout the world. FAO predicts that global rice utilization would increase over the next few years. Considering rice's importance, in this paper, a mixed-integer linear mathematical model for designing and planning of rice supply chain is proposed which aims to maximize total profit by integrating different decisions of the rice supply chain including supplier selection, cropping, fertilizing, pest control, harvesting, milling, transportation, and distribution. This model considers different rice varieties and takes into account irrigation water requirements of crops and available water resources. A case study of Iran farmlands in Gilan province is employed to show the applicability and advantages of the proposed model for the rice supply chain. Results indicate that increasing conversion ratio of paddy to rice and reducing labor costs would have a significant impact on the total profit of the supply chain.

Globalization and increasing competition in global markets have forced businesses to provide a high level of service to their customers. Time-sensitive transportation systems which are used in transportation of perishable goods, express mail delivery, and emergency services are playing a very important role in this regard. This paper addresses the problem of uncapacitated multiple allocation p-hub center problem (UMApHCP) which is fundamental in proper functioning of time-sensitive transportation systems. A mixed-integer programming formulation is proposed for the problem and a highly efficient Benders decomposition algorithm is developed for solving it. The proposed algorithm is capable of solving large-scale instances of the problem to optimality in order of seconds.

Forecasting stock index has been received great interest because an accurate prediction of stock index may yield benefits and profits for investors, economists and practitioners. The objective of this study is to develop two efficient forecasting models and compare their performances in one day-ahead forecasting the daily Vietnamese stock index. The model development used the data across 9 years of the trading days. The developed models are based on two artificial intelligence techniques, including adaptive network based fuzzy inference system (ANFIS) and long short-term memory (LSTM). The performance indexes including RMSE, MAPE, MAE and R were used to make comparison of the models. The experimental results reveal that both models successfully forecasted the daily Vietnamese stock index with a high accuracy rate. The comparative results of the two models were then discussed and analyzed. It was found that the LSTM model outperformed the hierarchical ANFIS model in forecasting stock index of the Vietnamese stock market.

The purpose of this research is to develop an early warning system model that can anticipate the occurrence of delisting of Islamic stocks (ISSI) using Support Vector Machines (SVM). Financial variables used consist of debt to equity, return on invested capital, asset turn over, quick ratio, current ratio, return on assets, return on equity, leverage, long term debt, and interest coverage. The population of this study is 335 sharia shares registered at ISSI in the period 2012-2017, with a total sample of 102 companies. The results show that the financial variables had a predictive power to the occurrence of delisting of Islamic stocks in the ISSI index. The effect of the independent variable or predictor variable is the financial ratio to the target variable or the dependent variable that is the potential for delisting of Islamic stocks in the ISSI index. With the development of 4 SVM models with different levels of prediction accuracy, SVM Model 1 with an accuracy rate of 71.57%, SVM Model 2 with an accuracy rate of 72.55%, SVM Model 3 with an accuracy rate of 82.35% and SVM Model 4 with an accuracy rate of 100%, it can be concluded that the SVM Model 4 is the best model.

This paper presents a new method for selection of optimal options portfolios. The problem of defining optimal portfolios of real options is formulated as integer programming. The algorithm of generating an optimal portfolio of real options is also presented. The incremental benefit of portfolio of real options is valued using Monte Carlo simulation and modeling the prices and demand as Geometric Brownian Motion. The presented method allows to select optimal portfolios of real options with consideration of statistical and qualitative dependences of options. The results show that real options can generate a significant increase in the net present value (NPV).

Vendor-managed inventory (VMI) is one of effective and crucial tools to alleviate the demand volatility of stocks problems, reduce time and operating cost in healthcare sector. VMI strategy becomes a necessity for both suppliers and hospitals to sustainably develop and to cope with stock availability and overall reliability process by sharing information. The process and management of VMI is a complicated work which needs substantial degrees of collaboration, expertise, and information sharing. This paper purposes a comprehensive multi-criteria decision making (MCDM) to select the best potential supplier for VMI collaboration in healthcare organization. The study developed MCDM framework consists of (i) Fuzzy Delphi approach to select the appropriate evaluation criteria for VMI supplier selection (ii) Fuzzy Step-wise Weight Assessment Ration Analysis (SWARA) method to determine the relative importance weight of evaluation criteria, (ii) Fuzzy Complex Proportional Assessment of Alternatives (COPRAS) to compare, rank and select the best appropriated supplier. An empirical case study was applied for a local famous public hospital and the best potential supplier was selected. The study reveals that the most evaluation criteria when selecting supplier for VMI in healthcare sector are institutional trust, information sharing and exchanging as well as information technology.

This study aims at investigating the impact of globalization on CO₂ emission in Vietnam. Empirical analysis is performed by employing autoregressed distributed lag approach on time series data for the period of 1990 to 2016. The paper tested the stationary, cointegration of time series data and utilized autoregressed distributed lag modeling technique to determine the short run and long run relationship among CO2 emission, globalization, foreign direct investment, exports, coal consumption per capita and fossil fuels electricity generation. The results show that globalization increases CO₂ emission in Vietnam and thus globalization is not beneficial for the long-term environmental health. Exports lowers CO₂ emissions in both short run and long run whereas coal consumption per capita and fossil fuels electricity generation raise CO₂ emissions. The study further shows that foreign direct investment did not affect CO₂ emissions directly in short run as well as in long run.