Route Optimization And System Implementation Of Cold Chain Logistics Distribution Based On Ant Colony Algorithm

Vehicle routing problem is the key issue of the combinatorial optimization in transportation,which has been widely applied in logistics and other fields.In recent years,with the enhancement of people's requirement in life quality,more and more concerns are focused on how to transport the fresh products to the designated location within specified time while ensuring quality.Therefore,the study of cold chain logistics and distribution becomes a practical need.Compared with traditional vehicle routing problem,the cold chain logistics distribution should consider not only the total distance of the vehicle,but also the insurance of fresh product quality in the transporting process and the delivery time that should be within the scope specified by the customers.The cold chain logistics distribution problem is characterized by its more complex models and more varied data.In this thesis,a mathematical model is built for the actual situation of cold chain logistics distribution,and an improved ant colony algorithm is proposed and carried out in experimental research.In the cold chain logistics distribution model established in this paper,the customer's penalty cost will be generated when the delivery time of the product exceeds the given time window,which is a reflection of both the quality of service and the quality of fresh products.However,it is difficult to balance the vehicle path with the shortest distance under the constraint of the customer's delivery time.Therefore,this problem is modeled as the constraint optimization problem with the goal of minimizing the total cost.It is composed of four parts: fixed cost,transportation cost,customer penalty cost and cargo cost,indirectly reflecting the basic operating costs during the transport process,the total length of the path and customer satisfaction.In order to solve this problem,an improved ant colony algorithm based on ant colony framework is proposed.The algorithm makes a corresponding change in pheromone updating,path selection and initial calculation,effectively improving the local optimization and the slow speed of initial solution generating in original ant colony frame,and better embodying the positive feedback properties of ant colony algorithm.In this thesis,the relevant parameters are discussed,mainly through the variable controlling method to determine the values of pheromone heuristic factors,expected heuristic factors,and pheromone volatile coefficient.We compare the ant colony algorithm with that using our strategy to show the correctness and feasibility of the improved strategy.We also make comparison with genetic algorithm to show our strategy is suitable to solve the path planning problem in cold chain logistics distribution,which is more competitive than the genetic algorithm.In order to better exhibit the results of this paper,a cold chain logistics distribution system is designed.The system has a friendly user interface,which can show the routing result of ant colony algorithm for the problem,including the final path allocation result and cost,the pie graph of relevant cost,and iterative graphs.