10.3389/fict.2019.00013.s001
Sebastian Feld
Christoph Roch
Thomas Gabor
Christian Seidel
Florian Neukart
Isabella Galter
Wolfgang Mauerer
Claudia Linnhoff-Popien
Data_Sheet_1_A Hybrid Solution Method for the Capacitated Vehicle Routing Problem Using a Quantum Annealer.pdf
2019
Frontiers
capacitated vehicle routing problem
CVRP
quantum annealing
hybrid solution method
clustering
routing
2019-06-25 04:26:13
article
https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_Hybrid_Solution_Method_for_the_Capacitated_Vehicle_Routing_Problem_Using_a_Quantum_Annealer_pdf/8317430
<p>The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that has been of great interest for decades for both, science and industry. The CVRP is a variant of the vehicle routing problem characterized by capacity constrained vehicles. The aim is to plan tours for vehicles to supply a given number of customers as efficiently as possible. The problem is the combinatorial explosion of possible solutions, which increases superexponentially with the number of customers. Classical solutions provide good approximations to the globally optimal solution. D-Wave's quantum annealer is a machine designed to solve optimization problems. This machine uses quantum effects to speed up computation time compared to classic computers. The problem on solving the CVRP on the quantum annealer is the particular formulation of the optimization problem. For this, it has to be mapped onto a quadratic unconstrained binary optimization (QUBO) problem. Complex optimization problems such as the CVRP can be translated to smaller subproblems and thus enable a sequential solution of the partitioned problem. This work presents a quantum-classic hybrid solution method for the CVRP. It clarifies whether the implementation of such a method pays off in comparison to existing classical solution methods regarding computation time and solution quality. Several approaches to solving the CVRP are elaborated, the arising problems are discussed, and the results are evaluated in terms of solution quality and computation time.</p>