Optimization Of Cold Chain Logistics Multi-objective LRPTW Problem Considering Customer Value

Recently,with the rapid development of the economy,fierce competition in the market environment and more and more diversified lifestyles,people's demand for food diversification,safety and rapidity is increasing.Cold chain logistics(CCL)develop rapidly.CCL as a special form of logistics,it's CCL system is imperfection,the transportation time is longer,the transportation cost is high,the transport process is easy to produce food spoiled and other issues,all of this directly impact on sales and customer satisfaction(CS)of cold chain products,it's hard to ensure the customer's requirements for the fresh of CCL products.Will,in the CCL location and distribution,the rationality of the location and the efficiency of distribution directly affect the distribution time,the freshness of the CCL products and the CS.Therefore,the CCL location and distribution play an important role to meet the freshness requirements of the frozen products.Most of the traditional CCLLRP only consider minimizing the transportation cost of CCL,but neglect customer service quality,CS and CV which related to the development of the enterprise market.Our paper is aiming at improving the freshness of food,reducing food spoiled,improving CV,establishing CCL LRPTW optimization model.Based on the traditional CCLLRP model does not consider the CS,CV and customer time window,our paper analyzed the composition of CV,based on considering the CCL cost,CS and CV,we proposed multi-objective LRP model which maximize CS and CV,minimize the total cost.Among this,the cost of CCL considered refrigeration cost,damage cost and penalty cost in addition to considering the traditional storage cost,transportation cost,CV is the sum of the actual value and potential value,the greater the CV the greater the profit of enterprise.Our paper uses weight method transform the multi-objective model into single objective model.The LINGO software is used to calculate a small-scale case and compare the results of the model which with and without considering the CV,highlighting the accuracy,effectiveness and practicability of the model.Finally,a genetic algorithm is proposed,which is verified by Solomon standard data.Finally,these results prove the stability,accuracy and effectiveness of our algorithm.