Road Network Optimization of Intelligent Warehouse Picking Systems Based on Improved Genetic Algorithm

Abstract

Intelligent Warehouse Picking System based on logistics robots is a new type of parts-to-picker order picking system, where robots carry mobile shelves to stationary pickers. The new picking mode puts forward higher requirements for the layout and design of warehousing network. In the existing few research on the path network optimization under the intelligent warehouse picking mode, the turning factors which obviously affects the picking efficiency, are seldom considered. In this paper, a mathematical model minimizing the total travel distance of logistics robots to complete all picking tasks is established, where the turning of robots is transformed into travel distance by cost function. Then an improved genetic algorithm with temperature parameter T and Metropolis acceptance criterion is proposed to solve the road network planning model. Finally, MATLAB is used to simulate and compare different road network layout strategies and algorithms from the total picking distance and total picking time to verify the effectiveness of the proposed method.