Quantum Annealing for Vehicle Routing Problem with weighted Segment

TL;DR

This paper formulates a vehicle routing problem with weighted segments as a QUBO model and demonstrates its effectiveness in reducing traffic congestion using quantum annealing on a D-Wave system.

Contribution

It introduces a novel QUBO formulation for vehicle routing with weighted segments to optimize traffic flow and congestion reduction.

Findings

Quantum annealing effectively finds optimal routes.

QUBO model reduces traffic congestion.

Simulations show improved route distribution.

Abstract

Quantum annealing technologies aim to solve computational optimization and sampling problems. QPU (Quantum Processing Unit) machines such as the D-Wave system use the QUBO (Quadratic Unconstrained Binary Optimization) formula to define model optimization problems for quantum annealing. This machine uses quantum effects to speed up computing time better than classical computers. We propose a vehicle routing problem that can be formulated in the QUBO model as a combinatorial problem, which gives the possible route solutions increases exponentially. The solution aims to optimize the vehicle's journey to reach a destination. The study presents a QUBO formulation to solve traffic congestion problems on certain roads. The resulting route selection by optimizing the distribution of the flow of alternative road vehicles based on the weighting of road segments. Constraints formulated as a condition for the level of road density. The road weight parameter influences the cost function for each road choice. The simulations on the D-Wave quantum annealer show optimal results on the route deployment of several vehicles. So that each vehicle will be able to go through different road options and reduce road congestion accurately. This solution provides an opportunity to develop QUBO modeling for more complex vehicle routing problems for road congestion.