Optimal scheduling for palletizing task using robotic arm and artificial bee colony algorithm

Palletizing using robotic arms is a common aspect of industrial robotization. Due to its efficiency, the robotic arm is often able to handle more then one production line. In such a case, the proper decision of selecting an item from one of several production lines will affect the overall efficiency. In this paper, three production lines handled by a single robotic arm are considered. Cycle time and maximum allowable waiting time of each item is taken into account. The authors proposed four different objective functions related to possible requirements in a factory environment, which led to constrained multi-objective optimization problems. To solve such a problem, the Artificial Bee Colony algorithm supported by Deb's rules has been applied. The obtained results have been compared with three basic decision mechanisms, and also with the Reinforcement Learning approach. It was shown that the proposed approach significantly increases the production rate and satisfies the particular requirements, i.e., minimum energy per palletized item ratio, equality of containers' filling.

Automated summary

This paper focuses on the palletizing problem using robotic arms, considering three production lines. By proposing four objective functions and applying the Artificial Bee Colony algorithm, the authors solve the constrained multi-objective optimization problem. Experimental results show that the proposed approach significantly improves the production rate and satisfies specific requirements.