The Network Design Of Low Carbon Reverse Logistics With Grey Uncertainty

With the full development of modern economy and technology,the material and cultural level of the people has been significantly improved.However,.in recent years,with the rapid decline of resources and the deterioration of ecological environment,the contradiction between economic development and environmental protection has become increasingly acute.Therefore,the concept of low-carbon economic development with the core of "reducing carbon dioxide and other greenhouse gas emissions and protecting the ecosystem" should come into being.The high carbon logistics industry has once again become one of the primary objectives of carbon reduction.Reverse logistics,which is based on recovering and refabricating waste products,can not only validly decrease the waste of natural resources and carbon emissions,but also reduce the cost of logistics and realize lean production of enterprises.Therefore,the research on the design of low-carbon reverse logistics network considering carbon emissions has a positive practical significance for the development of green,circular and sustainable low-carbon economy,strengthening the rational utilization of natural energy resources and promoting the economic benefits of enterprises.The reverse logistics,which mainly focuses on recycling waste products,is a serious threat to the recycling market order due to various uncertainties.Therefore,this paper aims to design a low-carbon reverse logistics network for the situation that the quantity,quality,and sorting efficiency of waste electronic products are gray uncertain in the recycling process.The grey chance constrained programming model with two grey variables(quantity and quality of recovery with grey uncertainty)and three grey variables(quantity,quality and grey sorting efficiency)are established respectively.Both take into account the planning of the distribution routes of the refurbished products and the transportation volume of the corresponding consumer market,and the latter adds to the consideration of the situation that the facility may have inventory costs and out-of-stock costs.Then,according to the characteristics of the above two models,the multi-objective hybrid genetic optimization algorithm based on grey simulation is used to design the solution algorithms respectively.Finally,a simulation experiment was conducted to verify the rationality of the model and the effectiveness of the algorithm,and the sensitivity analysis of the confidence level of important parameters was carried out,and favorable conclusions were drawn.The research of paper is of certain reference significance for enterprises to consider cost,carbon emission and environmental responsibility when planning and designing reverse logistics network in practice.