Research On Cold Chain Vehicle Routing Problem With Grey Demand And Customer's Aversion

The cold chain vehicle routing problem has a wide range of practical foundation and application,and it has always been one of the hot issues in the logistics field.There are many uncertainties of customer demand in practical problems.the existing research mainly focuses on random uncertainty and fuzzy uncertainty,there is less research on grey uncertainty.At the same time,due to the perishable nature of cold chain products,the level of customer's service is one of the important indicators for cold chain distribution companies.In the related research of customer's service levels,the research on customer's satisfaction has been quite extensive,there is little research on aversion.Therefore,the study of the cold chain vehicle routing problem considering the grey demand and customer's aversion degree has good theoretical and practical significance.The main work of this article is as follows:(1)The characterization of cold chain optimization problems.?Using the grey parameters to deacribe the uncertainty of customer's demand;?The quality of the cold chain products is optimal during the initial collection phase,as time passes,the aversion caused by the decreasing in product quality gradually increases.The metamorphic function of the cold chain products based on Arrhenius Equation is constructed,and further defining the customer's aversion function.(2)Single target cold chain VRP model and algorithm design considering grey demand.Firstly,aiming at the problem of grey uncertainty in the demand of cold chain logistics,aiming at minizing the total costs including fixed cost?transportation cost?demaging cost and refrigeration cost,the single-objective cold chain vehicle routing optimization model with grey demand is constructed.Secondly,the grey chance constrained programming theory is introduced and the grey optimization model is transformed into a grey chance condtrained programming model.Finally,grey simulation technique and particle swarm optimization algorithm are combined to design a solution algorithm.(3)Multi-objective cold chain VRP model and algorithm design considering grey demand and customer's aversion.For the cold chain vehicle routing problem with grey demand,in order to take into account the level of customer's service,aiming to minimizing costs and minimizing customer's aversion,a multi-objective cold chain vehicle routing optimization model was constructed.In order to reduce the probability of invalid path generation,based on the grey simulation technology and particle swarm optimization algorithm,adaptive grid algorithm was introduced to carry out a specific solution algorithm design for the model.(4)The simulation experiment shows that:?In the cold chain vehicle routing problem,it is necessary to take into account the customer's aversion,and it can help the cold chain distribution company to balance the multiple benefits.?The use of grey simulation technology to process the grey optimization model can take into account the flucation of customer's demand and improve the effection of cold chain vehicle routing decisions.? Different levels of confidence in grey simulation technology have an impact on cold chain decision-making.The higher the confidence level,the higher the total cost of cold chain distribution.Cold chain companies need to choose their own confidence level in combination with their own risks and affordability.?Using grey correlation and AHP analysis to evaluate non-inferior solution sets of multiple objectives,the two evaluation methods have different focuses,and the evaluation results will have certain differences.Cold chain distribution companies should select appropriate evaluation methods according to their needs.