Optimizing the Production of Test Vehicles using Hybrid Constrained Quantum Annealing

Abstract

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 by 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 CBC (Coin-or branch and cut) and Gurobi. We conclude that the performance of the CQM solver is comparable to classical solvers in optimizing the number of test vehicles. As an extension to the problem, we describe how the scheduling of the tests can be incorporated into the model.

Hamiltonian-Oriented Homotopy QAOA

“…AQC in the form of quantum annealing has been used for a variety of applications including real-world problems [36][37][38][39][40][41], and in quantum chemistry [42]. For a rigorous review of AQC check [31,43].…”

Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)

“…The QUBO model can be applied to a wide range of combinatorial optimization problems that are known to be NPhard, such as the maximum cut, minimum vertex cover, multiple knapsack, and graph coloring problems. Its applications span a diverse set of domains, including the automotive industry [74], portfolio optimization [75]- [77], traffic flow optimization [77], job scheduling [78]- [80], railway conflict management [81], bioinformatics [82], and others [83]. An extensive list of QUBO formulations for interesting problems can be found in [84].…”