Abstract—In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.
Index Terms—Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.
The authors are with the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Selangor, Malaysia (e-mail: hsadrnia@yahoo.com)
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Cite:A. Sadrnia, H. R. Soltani, N. Zulkifli, N. Ismail, and M. K. A. Ariffin, "A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems," International Journal of Engineering and Technology vol. 6, no. 3, pp. 536-540, 2014.
