Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm

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 o...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 24; no. 7; pp. 7564 - 7573
Main Authors: Azad, Utkarsh, Behera, Bikash K., Ahmed, Emad A., Panigrahi, Prasanta K., Farouk, Ahmed
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
combinatorial optimization
Critical components
Industrial applications
Industry 4.0
Intelligent transportation systems
Ising model
Optimization
Optimization algorithms
Program processors
quantum approximate algorithms
Quantum computing
Qubit
Qubits (quantum computing)
Routing
Supply chains
Traffic management
variational quantum algorithms
Vehicle routing
Vehicle routing problem
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary: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 <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula>, initialization of variational parameters, and problem instance itself.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3172241