Energy-Optimal Timing of Stochastic Robot Stations in Automotive Production Lines

Paper i proceeding, 2022

This paper investigates the problem of reducing the energy use of robot stations with stochastic execution times in production lines in automotive factories. First, real stochastic cycle time data is used to analyze and improve the cycle time. The result shows that the cycle time mean can be decreased with the cost of an increase in cycle time variance. Second, the cycle time data is combined with energy models of real robot stations. A stochastic optimization problem is formulated where the goal is to reduce the energy use of the stations in the production line by lowering the robot velocities, while not affecting the cycle time of the production line. The optimization problem is solved and the resulting energy optimized station is simulated using the improved cycle time. The result shows that up to 23 percent of the energy use can be reduced by only marginally affecting the cycle time variance of the production line.