Optimizing the Production of Test Vehicles using Hybrid Constrained Quantum Annealing

TL;DR

This paper models the automotive test vehicle configuration problem as a satisfiability problem and uses a hybrid quantum-classical solver to optimize vehicle production, comparing its performance with classical solvers.

Contribution

It introduces a hybrid constrained quadratic model solver for vehicle configuration optimization and benchmarks its performance against classical solvers.

Findings

CQM solver performance is comparable to classical solvers.

The model can incorporate test scheduling.

Demonstrates quantum-inspired optimization in automotive industry.

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.