Online scheduling algorithms for improving performance of pick-and-place operations on a moving conveyor belt

In many industrial applications, robotic systems accomplish the task of sorting items on moving conveyor belts. The list of objects to be gripped can be viewed as a queue of clients waiting to be served. The main peculiarities of this queue are that the serving times of its elements vary in a dynamic way, and that any client has to be served before it exits the robot workspace. In most practical cases, a simple first-in-first-out (FIFO) rule can be used for scheduling the jobs in the queue without dealing at all with the above issues. However, there are situations of industrial interest, as in the automatic sorting of wasted material, where the stochastic behavior of items flow gives rise to repeated overload situations, where the FIFO rule performs very inefficiently, requiring different scheduling strategies. In this paper, we propose two innovative online scheduling rules, based on suitable modifications of standard strategies for static queues, having the same complexity, but improved performance in the considered dynamic case. Simulation results confirm the validity of the proposed techniques