Optimizing the Production of Test Vehicles Using Hybrid Constrained Quantum Annealing

Optimization of pre-production vehicle configurations is one of the challenges in the automotive industry. Given a list of tests requiring cars with certain features, it is desirable to find the minimum number of cars that cover the tests and obey the configuration rules. In this paper, we model the problem in the framework of satisfiability and solve it utilizing the newly introduced hybrid constrained quadratic model (CQM) solver provided by D-Wave. The problem definition is based on the “Optimizing the Production of Test Vehicles” use-case given in the BMW quantum computing challenge. We formulate a constrained quadratic model for the problem and use a greedy algorithm to configure the cars. We benchmark the results obtained from the CQM solver with the results from the classical solvers like coin-or branch and cut and Gurobi solver. We conclude that the performance of the CQM solver is comparable to the classical solvers in optimizing the number of test vehicles, given the noise-prone quantum hardware. However, the CQM solver takes much more time, which prohibits obtaining useful quantum advantages. As an extension to the problem, we describe how the scheduling of the tests can be incorporated into the model.