Picking Operation Optimization And Algorithm Research Based On Kiva System

With the continuous development of e-commerce, the requirements for the automaticity and work capacity of distribution center have increased much more. Traditional distribution center picking system with high automation has shown shortcomings in the process of their application.Aiming at solving the shortcomings, Kiva system comes into being, which has been applicated maturely in the overseas distribution center, while hasn’t been quoted domestic. The main contents completed in the paper that based on the study of Kiva system operation mode following:First, the paper gives two strategies to solve the multi-robots path planning of Kiva picking system. One is the improved A*algorithm, which has gives the corresponding collision avoidance mechanism and the possible solution of back and forth trip. The improved algorithm can well solve path planning problem in the Kiva picking system with multiple robots. The other is the method by specifying the car traffic rules to plan path.Second, the paper analyses and improves task allocation and path planning of Kiva picking system of with multi-robots. The paper has given four Kiva picking system operation strategies:the model of task allocation in sequence connecting with improved A*algorithm for path planning, the model of task allocation in sequence connecting with traffic rules path planning, the model of task allocation by the nearest connecting with improved A*algorithm for path, the model of task allocation by the nearest connecting with traffic rules path planning.Third, for a concrete example, the paper builds different MATLAB simulation models which connected complete set strategies of picking operation in Kiva system, and analyses simulation results. Different solutions is made of different strategies, through building models with different strategies and analyses and compares simulation results to choose a better optimized picking operation plan.In a word, this paper provides a feasible and better method to solve the task allocation problem in Kiva picking system. For the independent study of Kiva system in the future, the methods that studied in the paper have a certain reference value.