  This study focuses on the energy optimization of industrial robotic cells,
which is essential for sustainable production in the long term. A holistic
approach that considers a robotic cell as a whole toward minimizing energy
consumption is proposed. The mathematical model, which takes into account
various robot speeds, positions, power-saving modes, and alternative orders of
operations, can be transformed into a mixed-integer linear programming
formulation that is, however, suitable only for small instances. To optimize
complex robotic cells, a hybrid heuristic accelerated by using multicore
processors and the Gurobi simplex method for piecewise linear convex functions
is implemented. The experimental results showed that the heuristic solved 93 %
of instances with a solution quality close to a proven lower bound. Moreover,
compared with the existing works, which typically address problems with three
to four robots, this study solved real-size problem instances with up to 12
robots and considered more optimization aspects. The proposed algorithms were
also applied on an existing robotic cell in \v{S}koda Auto. The outcomes, based
on simulations and measurements, indicate that, compared with the previous
state (at maximal robot speeds and without deeper power-saving modes), the
energy consumption can be reduced by about 20 % merely by optimizing the robot
speeds and applying power-saving modes. All the software and generated datasets
used in this research are publicly available.
