Abstract
Green Supply Chain Management (GSCM) is the adopted by many companies due to the government policies of various countries. The optimization technique can be applied in the GSCM to increase the profit of the company. In this research, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) technique is applied for the optimization of GSCM to increase the performance. The NSGA-II method has the advantage of choosing the solution closer to the pareto-solution and uses the elitist technique to preserve the best solution in the next generation. Mathematical model of the GSCM system is established and data is provided as input to the mathematical mode. Data is generated in three types, small scale, medium scale and large scale. The proposed NSGA-II method has high performance in the optimization technique compared to existing method. The proposed NSGA-II method has the Number of Pareto Solution (NPS) metrics of 17 for large scale data, while existing method has 14.
Highlights
Government regulations or public environment awareness has enforced the companies to apply Green Supply Chain Management (GSCM) and green innovation
Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is proposed in GSCM for the multi-objective optimization
Hybrid optimization technique is applied in the GSCM system and has the lower performance due to limited number of pareto-solution
Summary
Government regulations or public environment awareness has enforced the companies to apply Green Supply Chain Management (GSCM) and green innovation. Both practices are important to apply in the companies to improve the environmental factors [1]. In this scenario, the management of companies are focusing on the GSCM to increase the efficiency in the process [2]. Optimization method is need to be applied to increase the efficiency and to maintain the economic regulation of the companies [5].
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