Multi-Objective Optimization Model And Application For Location-Routing-Inventory Problem In Cold Chain Logistics Network

At present,China has become a big country in the production and consumption of fresh agricultural products and the rapid development of cold logistics can provide necessary services and protection.But China's cold chain logistics industry started late,the cold chain equipment in some areas are relatively scarce and cold chain logistics management is relatively backward,which resulting in a "often broken chain" phenomenon.These problems have seriously affected the international competitiveness of agricultural products and the food safety of residents.Therefore,it is urgent to study on these problems systematically.As the core part of the cold chain logistics network,the distribution center location decision-making,inventory decision-making and transportation decision-making have close relationship,which means making a change of any decision will affect the other two decisions.With this background,the integrated optimization of location routing inventory in cold chain logistics network is not only conducive to improve the core competitiveness of enterprises,but also helpful to ensure the quality and safety of food.Based on the analysis of its mechanism,the dissertation studies a series of integration optimization problems in cold chain logistics network,builds the corresponding mathematical model,and improves the related algorithms to solve the problems.The main research includes the following aspects:First of all,for the basic location routing inventory problem in cold chain logistics network,the dissertation constructs a multi-objective optimization model for the least location cost,inventory cost and transport cost.Based on the PSO algorithm,the dissertation designs an improved discrete particle operator to solve the model.The algorithm applies comprehensive learning strategies which expands the search scope and maintain the diversity of population.The algorithm uses a local search operator of variable neighborhood search which improves the convergence speed,improves "premature convergence" problem in particle swarm algorithm and also improves the quality of the solution.The simulation results show that the algorithm is feasible and effective.Secondly,due to the characteristics of fresh agricultural products are perishable,the dissertation construct a multi-objective optimization model considering customer's requirements of soft time window which adds constraints of the customer time window based on the basic problem.Then the dissertation combines Pareto ant colony algorithm and genetic algorithm,proposes a improved multi-objective mixed ant colony algorithm.The algorithm improves ant state transition rules;improve the uniformity of individual distribution and global search ability of the algorithm.The simulation results show that the new algorithm can effectively improves the global search ability on the basis of the advantages of the ant colony.Finally,for the problem of high energy consumption in cold chain logistics,the dissertation proposes a multi-objective optimization model considering minimum location cost,minimum inventory cost,vehicle routing optimization and minimum carbon emissions,which introduces the carbon trading costs into the basic problems.Then the dissertation combines differential algorithm with NSGA-?,designs an improved NSGA-? algorithm to solve the model.The algorithm introduces dynamic crowding distance to calculate the degree of congestion,which improves the distribution uniformity of individuals.To prevent the algorithm from falling into local convergence,a mutation operator is designed to increase the diversity of population distribution.The simulation results show the feasibility and effectiveness of the new algorithm in solving the problem of carbon emission constraint.