Robotic-cell scheduling with pick-up constraints and uncertain processing times

Abstract

Technological developments have propelled the deployment of robots in many applications, which has led to the trend to integrate an increasing number of uncertain processes into robotic and automated equipment. We contribute to this domain by considering the scheduling of a dual-gripper robotic cell. For systems with one potential bottleneck, we determine conditions under which the widely used swap sequence does not guarantee optimality or even feasibility and prove that optimal schedules can be derived under certain conditions when building on two types of slack we introduce. With the addition of a third type of slack and the concept of fixed partial schedules, we develop an offline-online scheduling approach that, in contrast with previous work, is able to deal with uncertainty in all process steps and robot handling tasks, even under pick-up constraints. The approach can deal with single- or multiple-bottleneck systems, and is the first approach that is not restricted to a single predefined sequence such as the swap sequence. Our approach is well suited for real-world applications, since it generates cyclic schedules and allows integration into commonly-used frameworks for robotic-cell scheduling and control.

We demonstrate the applicability of our approach to cluster tools in semiconductor manufacturing, showing that our approach generates feasible results for all tested levels of uncertainty and optimal or near-optimal results for low levels of uncertainty. With additional symmetry-breaking constraints, the model can be efficiently applied to industrial-scale test instances. We show that reducing uncertainty to below 10% of the processing time would yield significantly improved cycle lengths and throughput. We also demonstrate that the widely used swap sequence only finds solutions for less than 1% of the instances when strict pick-up constraints are enforced and processing times are heterogeneous. As our approach finds feasible solutions to all of these instances, it enables the application of robotic cells to a significantly broader application environment.

Keywords:

• Robotic-cell scheduling
• automated manufacturing equipment
• uncertainty
• cyclic scheduling
• optimization

Additional information

Notes on contributors

Daniel Tonke

Daniel Tonke is a senior data scientist at the Boston Consulting Group. He graduated from the Technical University of Munich with a Ph.D. in industrial engineering, from the KAIST (Korea Advanced Institute of Science and Technology) with a M.S. in industrial and systems engineering, and from the Technical University Berlin with a Dipl.-Ing. in industrial engineering. His research interests are in manufacturing and logistics, with a focus on production planning and scheduling in semiconductor industries and process industries.

Martin Grunow

Martin Grunow is a professor of production and supply chain management at the Technical University of Munich’s TUM School of Management in Germany. He received his industrial engineering degree and his Ph.D. from the Technical University Berlin before joining the R&D department of Evonik Degussa, a producer of special chemicals. Later, he was a professor and department head at Technical University of Denmark. His research interests are in manufacturing and logistics with a focus on the electronics and automotive sectors as well as on the process industries, including chemicals, pharmaceuticals, and food.

Renzo Akkerman

Renzo Akkerman is an associate professor in operations research and logistics at Wageningen University in The Netherlands. Earlier, he was a professor in operations management and technology at the Technical University of Munich in Germany and an associate professor in operations management at the Technical University of Denmark. He graduated from the University of Groningen in The Netherlands with a Ph.D. in operations management and an MSc in econometrics and operations research. His (often interdisciplinary) research mostly deals with operations management in process industries, with topics ranging from planning and scheduling to supply chain design