Green Supply Chain Management Optimization Based On NSGA-II Method
S. Sundar1, C. Dhanasekaran2, S. Sivaganesan3
1S. Sundar*, Department of Mechanical Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India.
2Dr. C. Dhanasekaran, Department of Mechanical Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India.
3Dr. S. Sivaganesan, Department of Mechanical Engineering, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India
Manuscript received on November 25, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 210-216 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3092129219/2019©BEIESP | DOI: 10.35940/ijeat.B3092.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Green Supply Chain Management, Non-dominated Sorting Genetic Algorithm -II, Elitist technique, Mathematical model, and Number of Pareto Solution.