Abstract: In order to promote the application of fishbone warehouse in actual scenarios, aiming at the problem of picking route optimization under the fishbone warehouse layout, a distance calculation model for picking points and a picking route optimization model based on the shortest multi-vehicle picking distance with load and volume restrictions as the overall goal were constructed. Considering the strong global search ability of the genetic algorithm (GA), the fast convergence speed of the particle swarm optimization (GAPSO) and the strong local optimization ability of the ant colony algorithm (ACO), a hybrid algorithm to solve the optimization model of the picking route was proposed. Through simulation experiments of different order sizes, it is concluded that the hybrid algorithm is superior to the GA algorithm and the GAPSO algorithm in terms of fitness value, number of iterations, and convergence speed. And when the order size is large, the average fitness value is reduced by about 8%, which effectively shortens the total picking distance. The results verify the advancement and effectiveness of the hybrid algorithm in solving the picking route problem under the fishbone warehouse layout, and provide new solutions and ideas for solving the picking route problem inside such warehouses.

Key words: fishbone warehouse layout, a distance calculation model for picking points, a picking path optimization model, GA-PSO-ACO hybrid algorithm