Research On Order Batching And Picking Route In Fishbone Warehouse Layout

As the operation layer of warehousing operations,order batching and picking route optimization have always been the focus of research in the field of warehousing optimization.Good order batching schemes and efficient planning of picking routes can significantly improve warehouse operation efficiency and reduce operating costs.As of now,most of the research on order batching and optimization of picking routes is based on traditional layouts,and few involve non-traditional layouts.This article focuses on the Fishbone non-traditional warehouse layout,studies the order batching and picking route issues,considers the realistic constraint of the picking equipment's limited load,and takes the total picking distance of all order picking tasks as the objective function to establish the order batching mathematical model,according to the characteristics of Fishbone layout,a non-traditional layout S-shape route strategy is proposed,and the equipment adopts the non-traditional layout S-shape strategy when picking items.Then a genetic algorithm is designed to solve the order batching problem,which is elaborated in detail.The design ideas and processes of encoding and decoding,fitness function,selection operator,crossover operator,mutation operator,etc.According to whether the load capacity of the picking equipment is considered to be limited,the picking route problem is divided into a single vehicle for an order and multi-vehicles for an order.In both cases,a mathematical optimization model was established for them,combined with the geometric characteristics of the Fishbone storage layout,the formula for calculating the distance between any two cargo positions in the Fishbone layout was proposed and a non-traditional layout cargo distance matrix was constructed.An ant colony optimization algorithm solves the picking route problem in the case of a single vehicle for an order and multi-vehicles for an order.Since a single vehicle for an order is a special case under the assumption that the loading capacity of the picking equipment is infinite based on the multi-vehicles for an order.The solution steps of the ant colony algorithm are listed in detail only for the multi-vehicles for an order.A large number of simulation experiments are carried out based on MATLAB to verify the effectiveness of target algorithms.For the order batching calculation examples,the picking by an order batching strategy and the first-come-first-select batching strategy are selected as the comparison methods of the genetic algorithm.The experimental results show that the genetic algorithm has certain robustness while ensuring the best results.For the picking route examples,the non-traditional layout S-shape strategy and genetic algorithm are selected as the comparison methods of ant colony optimization algorithm.By analyzing the evolution curve of algorithms and the experimental data,it is concluded that the ant colony optimization algorithm has excellent performance in optimization and convergence performance,which effectively shortens the traveling distance.