Research On The Optimization Of The Picking Strategy In The Front Warehouse Of The Supermarket

In recent years,China's retail market has undergone profound changes,and “new retail” has become a development trend.Beginning in the second half of 2016,the Internet giants,led by Ali and Tencent,joined hands with traditional merchants to enter the era of “online to home”.Store front warehouse is an important transit station for online business home service.It carries orders such as order picking,picking,customer service,delivery handover,stocking,replenishment,etc.,all of which need to be completed in the warehouse.For the front of the warehouse.The optimization of frontend warehouse performance is of great significance to improve the working efficiency and service level of the overall process operation.Among the links included in the pre-acquisition,picking is the most expensive and longest-consuming part.The focus of this paper is on the optimization of order batching and picking paths in the picking work.Based on the research and business status in the past,this paper establishes the order batching and picking path optimization model under the condition of multiple picking equipment and multiwarehouse storage areas,and finally applying these two optimizations to the same example.The walking distance required should be minimized during the picking process.For the order batch problem,this paper takes the shortest picking distance as the objective function,and establishes the optimization model in the dual-zone warehouse where multiple devices exist together.The genetic algorithm for solving this problem is proposed,and the chromosome coding,crossover method and mutation method are designed in detail.For the problem of picking path optimization,this paper also establishes the optimization model with the shortest picking path as the objective function,and discusses the situation that there may be single or multiple picking equipment in the actual operation process,and designs the genetic algorithm to solve the problem in detail,and solves the model.Finally,the effectiveness of the optimization model and the algorithm in this paper is verified by an example.Firstly,the validity of order batching is verified.The three methods of order picking,first-come first picking,and genetic algorithm order batching are compared,and the results are sorted by picking walking distance: batching based on genetic algorithm Picking <first-to-first batch picking <single picking,because the picking time is the shortest when the walking distance is the shortest,so the batch method designed in this paper is better than the traditional order batching method;at the same time,the picking path optimization The problem is verified,and the effectiveness of the picking path optimization of the genetic algorithm is calculated by the example.The walking distance of the picking path of the traditional S-type crossing strategy is also shorter,and the optimization effect is more obvious.After solving the calculation example of each model,the effect of joint optimization is verified by an example,which is to verify the effectiveness of order picking batch optimization and then picking path optimization.Finally,joint optimization can minimize picking.Walking distance in the process.In terms of programming,this paper uses Python coding to implement the genetic algorithm of two problems,and solves the order batching and picking path optimization model.Supermarket stores have a labor-intensive job,and the factors involved are complex.Therefore,in addition to the conditions and models assumed in the article,order batching and path optimization can also consider the factors such as warehouse layout,storage optimization,personnel workload and other iterations,which are the future directions for research and optimization.