Designing sustainable recovery network of end-of-life products using genetic algorithm

Sustainable development was articulated by the Brundtland Commission as development that “meets the needs of the present without compromising the ability of future generations to meet their own needs”. Consumers and legislation have pushed companies to re-design their logistics networks in order to mitigate negative environmental and social impacts. The objective of this paper is to design a sustainable recovery network, in which economical, environmental and social impacts are balanced. Life cycle analysis (LCA) has been applied to investigate the environmental impact of different end-of-life (EOL) options. Analytical hierarchy process (AHP) has been utilized to calculate social impacts. Next in this research, a three-objective mathematical programming model has been developed to maximize economic and social benefits and minimize negative environmental impacts, simultaneously. Scrap tires have been considered for a case study. Multi-objective genetic algorithm (MOGA) has been applied to find the Pareto-optimal solutions. In the recovery network of scrap tires, each solution corresponds to a different configuration of the network, based on technology type and location of installed recycling plants. These Pareto-optimal solutions will give some trade off information about the three mentioned objectives, so decision makers can assess the economical impact of efforts of improving the social and environmental issues.