Research on Stacker Route Planning Based on Ant Colony Optimal Matching Parameter Algorithm

  • Heying Bian,Xiaoli Zhang,Yingli Chang,Senpeng Cao
Keywords: Automated storage and retrieval system (AS/RS),Stacker,Ant colony algorithm,Path planning,Optimal matching parameter.

Abstract

In view of the automatic access requirement of a metrological verification center for a large number of automatic three-dimensional warehouses of instruments to be inspected, the operation mode of stacker in and out of the warehouse is analyzed in detail, and the mathematical model is established. Ant colony optimal matching parameter algorithm(ACOMPA) is acquired according to analyzing the influence of main parameters of ACOMPA such as m(ant colony number), α(Enlightenment factor), β (hoped heuristic factor), ρ(pheromone volatilization coefficient) and Q(pheromone release total). ACOMPA, random scheduling and genetic algorithm (GA) are used to compare the effects of stacker scheduling path in order to verify the effectiveness of ACOMPA. The simulation results show that the path of ACOMPA is reduced to 37.7% of random algorithm(RA) path and 82% of that of genetic algorithm, which is faster than genetic algorithm and has stronger global convergence ability. It can obtain the optimal solution in the scheduling path planning of stackers and improve the efficiency of stackers compound operation.

Published
2020-09-24
How to Cite
Heying Bian,Xiaoli Zhang,Yingli Chang,Senpeng Cao. (2020). Research on Stacker Route Planning Based on Ant Colony Optimal Matching Parameter Algorithm. Design Engineering, 356 - 370. https://doi.org/10.17762/de.vi.162
Section
Articles