Hybrid Computing and Decision Technologies in Improving Accuracy of Structural Equation Model for Sustainable Environmentally Friendly Product Management

Main purpose of this research is to study influential variables of green innovation strategy, corporate social responsibility, government policy, transformation leadership including human resource development on the success level in managing sustainable environmentally friendly products of industrial plants. The sequential procedures on statistical techniques are proposed with the survey data on both quantitative and qualitative research elements. Confirmatory factor and path analysis including the structural equation modeling are mixed to identify the causal relationship between variables and the dependent variables. In this article, two metaheuristic algorithms namely sequential evolutionary elements based on variable neighborhood search and particle swarm optimization algorithms are proposed to enhance the sustainable environmentally friendly product management model. The results show that all performance measures of the particle swarm optimization algorithm are better, but not statistically significant when compared. Evolutionary elements from Metaheuristic approaches are the powerful tool for generating the management model and aiding the industries for decision making. The qualitative research was from the multistage sampling and in-depth interviews to finally provide guidelines for managing environmentally friendly products. From the numerical results all of proposed variables affected the success at a high level of opinions. The results of this research will be efficiently used to promote sustainable environmentally friendly products for the manufacturing in Thailand.