Optimization Method And Application Of Coal Product Logistics Park Dynamic Site Selection Based On PI-PSO

Coal, as one of the most important energy sources, is closely related to social production and people’s life. Although the important position of coal has been replaced by oil, but due to gradual depletion of oil resources, while coal because of the huge reserves, coupled with coal gasification technology is becoming more and more mature, coal will become in the production and life of human beings cannot be replaced one of the sources of energy. As a big country of coal production and consumption, China has gradually formed a certain scale of coal logistics supply chain system, but in recent years, the coal industry by the decline in market demand, the coal industry transformation and upgrading lag and other factors influence, the coal industry appears structural overcapacity and falling prices, enterprise losses and other problems, the speed up the transformation of coal enterprises has become a task of top priority. And the key link in the logistics supply chain as the coal industry transformation, only the effective integration of logistics resources, efficient use of logistics channel, in order to reduce the burden of the coal industry in the logistics supply chain. However, the whole coal logistics supply chain is lack of core logistics park with organization and coordination, which leads to the rising of logistics cost. Therefore, it is imperative to integrate logistics resources effectively and build a large coal logistics park. In order to effectively save costs and improve efficiency, logistics park location is particularly important.From the domestic and foreign scholars on the coal logistics and distribution center location of the research, the paper analyzed the existing research focus and weak points,and this paper puts forward the key point of this research and clarifies the significance of the improved particle swarm optimization algorithm to the optimization of logistics park location. Organize the related concepts and theories of logistics management, coal supply chain, particle swarm optimization (PSO), collected the large amounts of data and information, to investigate the current status of management of coal logistics, according to the status quo analysis the problems existing in the management of coal logistics, including logistics park location problems, and problems relating to be affected due to location, clear of logistics park construction coordination scheduling functions necessary and scientific location of the importance and urgent problems. Reference to other industries of logistics park location principle, and combined with the characteristics of coal logistics and the principle of coal logistics park location is constituted. According to this principle to measure the final design of the program is in line with the standard, is feasible. Combining with the management status of coal enterprise product logistics and the problems to be solved, the multiple objective model is built with the goal of minimizing the cost, the shortest order response time, and the highest customer demand satisfaction rate, and using parasitic immune mechanisms based on MATLAB are embedded into particle swarm optimization algorithm hybrid algorithm for multi-objective model simulation. Collected by a research group in Shanxi XX demand data and cost data of dynamic location model is simulated, and from the static location and dynamic location, traditional algorithm and improved algorithm, coal logistics park location angle of the three principles of simulation results were analyzed. The results show that the dynamic location method and the improved particle swarm optimization algorithm can realize the optimization of logistics park location. Due to the explicit and implicit parameters present in particle swarm optimization algorithm, can realize the algorithm search space changes in the way through the adjustment of parameters. Therefore this paper adjusting inertia weight and learning factor to simulate and optimize the logistics park location. Finally, according to the simulation results, it puts forward some suggestions for the location and management of the coal logistics park.