Volkswagen: Workflow-optimization for quality control testing

Scheduling complex workflows is a challenge for any industry. Think about timetables in the logistics sector, for example, where delivery, packaging and pick-up depend on multiple stakeholders and sometimes unforeseen external factors like weather conditions. At stake are budgets, commitments to external partners and, last but not least, employee satisfaction.

NP-hard scheduling problem

In computing, this type of problem-solving is called an NP-hard scheduling problem. In the automotive industry, it presents a daily issue in context of the quality control process. Each manufactured car needs to pass a quality testing at the end of an assembly. A specifically trained workforce workers carries out several tests according to a certain order. The workflow is constrained by several factors, including the number of workers available over time and some tests being dependent on others being completed first.

The Volkswagen Group Data:Lab Munich has teamed up with our solution partner Terra Quantum to investigate this industrial use case. The joint research team compared problem instances with a number of quantum, classical, and hybrid quantum-classical algorithms. The team developed a novel QUBO to represent the scheduling problem, illustrating how the QUBO complexity depends on the input problem. They present a decomposition method for this specific application to mitigate this complexity, showing how effective the approach is.