Optimization Algorithm For Distribution Center Picking Path Based On RFID

Recent years, with the rapid development of the e-commerce in China, some B2Ccompanies also begin to build their own distribution center and distribution system. Thedistribution center is an important part of the entire logistics system, the speed of the logisticsand distribution is directly affected by distribution center efficiency. Logistics services directlyaffect the customer’s shopping experience and service satisfaction, and it’s also indirectlyreflects that customer’s trust to company brand, so it has become the most thorny issue thathow to effectively manage and improve the operational efficiency of the distribution center forenterprises.Distribution centers, including several aspects of internal operations, such as: receiving,handling, storage, picking, sorting, shipping, etc.. Picking is an important part of a largerdistribution center in the proportion accounted for internal operations, and also the mosttime-consuming job, it can be said that picking directly impacts on the efficiency of thedistribution of the overall operational efficiency and further impacts on service satisfaction ofconsumers. Firstly, in this dissertation, taking two-block warehouse as the research object,RFID technology is introduced to the cargo distribution center management system and itmore. Focus on the optimization for the picking routing of vehicle. Secondly, optimizationmathematical models are established, respectively for individual picking vehicles and multiple.Then, a appropriate algorithm be designed to optimize the picking routing of the vehicle andeffectively reduce the walking distance from the vehicle picking process.It shows that the hybrid genetic annealing algorithm genetic algorithm for a singlevehicle picking path optimization is proved more effectiveness compared to the geneticalgorithm and for multi-vehicle route optimization picking, the overall mathematical model isfirstly optimized by the path optimization hybrid genetic algorithm of a single vehicle, whichbatch of orders, is processed and then genetic algorithm is used for TSP of picking path ofeach vehicle so the walk distance will be minimized at the picking process in the case ofmultiple vehicles. In the aspect of programming and solving, MATLAB programming language is used toimplement related algorithms for solving picking path optimization model, and the simulationalgorithm works in MATLAB R2010a platform in the dissertation.