Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for optimally routing transportation of goods by vehicles at a given set of locations. This paper discusses the broader problem of vehicle traffic management, more popularly known as the Vehicle Routing Problem (VRP), and investigates the possible use of near-term quantum devices for solving it. For this purpose, we give the Ising formulation for VRP and some of its constrained variants. Then, we present a detailed procedure to solve VRP by minimizing its corresponding Ising Hamiltonian using a hybrid quantum-classical heuristic called Quantum Approximate Optimization Algorithm (QAOA), implemented on the IBM Qiskit platform. We compare the performance of QAOA with classical solvers such as CPLEX on problem instances of up to 15 qubits. We find that performance of QAOA has a multifaceted dependence on the classical optimization routine used, the depth of the ansatz parameterized by $p$ , initialization of variational parameters, and problem instance itself.
Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
IEEE Transactions on Intelligent Transportation Systems ; 24 , 7 ; 7564-7573
2023-07-01
1160279 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Solving a Vehicle Routing Problem using Ant Colony Optimization Based Algorithm
British Library Conference Proceedings | 2002
|Hybrid Heuristic Algorithm for solving Capacitated Vehicle Routing problem
BASE | 2014
|An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem
BASE | 2017
|Solving Vehicle Routing Problem with Time Windows by Genetic Algorithm
British Library Conference Proceedings | 2002
|