Reverse Logistics using Genetic Algorithm

Assumptions about natural resources, that they are unlimited and its regeneration by the environment can reimburse for all human behaviours is no longer agreeable. Sustenance, in the era of depleting natural resources, is thus a crucial issue for the upcoming generations. The logistics sector is not far away from exploitation of the natural conventional resources. The environmental regulations imposed on companies, have made them not only cautious about the reusing the worn out products, but also have made them realize, the economic benefits of reiterating. Reverse logistics, deals with the collection, recovering, recycling, remanufacturing and assessment of the used products. Hence, an emphasis on developing an efficient reverse logistics network and making the supply chain more competitive by benefitting from the economic advantage obtained has become an important area. In this research, there is a development of a new optimized model for minimizing fuel consumption in reverse logistics for gathering and recycling used automobile alternators in supply chain. Hence the sole objective of the research is to critically examining and developing a model which would give both the economic and environmental advantage simultaneously. This objective of obtaining economic and environmental benefits simultaneously can be achieved using the Genetic Algorithm (economical) technique and the Green Logistics Model (environmental). Genetic Algorithm techniques have been used for solving troublesome problems regarding the supply chain. Genetic Algorithm stands its application in the development of the multiechelon reverse logistics networks for the collection of the worn out products. The second model for decreasing the environmental consumption is the Green Logistics model. This model can be implemented by developing an advanced model for collection and the recycling of the automobile alternators and thus minimizing the fuel consumption in the transportation. Thus the research briefs on Green Logistics and Genetic Algorithm through a case study based approach.