The Network Optimization Of Low Carbon Closed-loop Supply Chain With Grey Recycling Quantity And Grey Recycling Quality

The network optimization of closed-loop Supply Chain(Closed Loop Supply Chain,CLSC)design has always been one of the topics of research scholars.Then,the network optimization problem of CLSC can be divided into the certainty and uncertainty,the research with random and fuzzy uncertainty more in-depth,relatively little grey uncertainty oriented research.In the meantime,as some countries have put forward and implemented the policy of low carbon constraints,carbon emissions trading mechanism has become a widely accepted effective emission reduction mechanism.Therefore,the research of network optimization of low-carbon CLSC considering the recycling product grey quantity and grey quality,has strong theoretical and practical significance.The main research work of this paper is as follows:(1)The processing of characteristics of the optimization problems on low carbon CLSC:?Considering recycling products with grey uncertainty,recycling quantity and quality be depicted as grey numbers.?Based on low carbon environment and carbon emissions trading mechanism,quantitative carbon trading costs,be included in the total cost of the CLSC distribution network.(2)The construction and solving model of low carbon CLSC optimization With grey recycling quantity.First,considering the recycling amount of product as grey uncertainty,establishment the network optimization of low carbon CLSC model under a carbon trading mechanism.Second,because of the gray optimization model cannot solve directly,base on the introduction of grey chance constrained programming and the whitening weight function of grey theory,put forward the whitening-equivalent class conversion method of grey chance constrained,and the whitening-equivalent model can be obtained,using Lingo software to solve directly.Finally,through simulation experiment,comparing with the general uncertainty model transformation method,the effectiveness of the whitening-equivalent class conversion method was verified.(3)The construction and solving model of low carbon CLSC optimization With grey recycling quality.First,considering the recycling quality of product as grey uncertainty,establishment the network optimization of low carbon CLSC model under a carbon trading mechanism.Secondly,combined with chance constrained programming theory,the grey optimization model is converted into a grey chance constrained optimization model.Due to the probability of quality sum to 1,the whitening-equivalent class conversion method on(2)cannot be used dealing with grey chance constrained,therefore presents a hybrid genetic algorithm based on grey simulation technology.Finally,through simulation experiment,The result of experiment show that:?It is effective using hybrid genetic algorithm to deal with complicated grey model.?The less confidence level of grey chance constrained,the weaker grey constraints,the smaller objective function value.(4)The construction and solving model of low carbon CLSC optimization With grey recycling quantity and quality.First,considering the recycling quantity and quality of product as grey uncertainty,establishment the network optimization of low carbon CLSC model under a carbon trading mechanism.Secondly,The model transformation and solving using grey chance constrained programming and hybrid genetic algorithm on(3).In addition,this paper analyzes theoretically the influence of carbon trading mechanism of CLSC network optimization.Finally,through simulation experiment,The result of experiment show that:?The less confidence level of grey chance constrained,the weaker grey constraints,the smaller total cost,the smaller Carbon emissions.?The closed-loop supply chain under carbon trading mechanism consider both economic costs and carbon emissions,can effectively make the whole closed-loop supply chain to reduce carbon emissions.