Optimization of the Storage Spaces and the Storing Route of the Pharmaceutical Logistics Robot

Abstract

Auto drug distribution systems are used popularly to replace pharmacists when drugs are distributed in pharmacies. The Cartesian robot is usually used as the recovery mechanism. Under non-dynamic storage location conditions, generally, the selected planning route of the Cartesian robot is definite, which makes it difficult to optimize. In this paper, storage spaces were distributed for different drugs, and the route of storing was broken down into multiple path optimization problems for limited pick points. The path was chosen by an improved ant colony algorithm. Experiments showed that the algorithm can plan an effective storing route in the simulation and actual operation of the robot. The time spent on the route by improved ant colony algorithm sequence (IACS) was less than the time spent of route by random sequence (RS) and the time spent of route by traditional ant colony algorithm sequence (ACS); compared with RS, the optimized rate of restoring time with iacs can improve by 22.04% in simulation and 7.35% in operation. Compared with ACS, the optimized rate of restoring time with iacs was even more than 4.70% in simulation and 1.57% in operation. To the Cartesian robot, the optimization has certain guiding significance of the application on the 3D for improving quality.