Open AccessGreen Supply Chain Management Optimization Based On NSGA II Method
Author(s) -
S. Sundar,
C. Dhanasekaran,
S. Sivaganesan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3092.129219
Subject(s) - sorting , multi objective optimization , mathematical optimization , pareto principle , computer science , genetic algorithm , profit (economics) , supply chain , supply chain management , scale (ratio) , industrial engineering , mathematics , engineering , algorithm , economics , business , physics , marketing , quantum mechanics , microeconomics
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.