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Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
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AI
DOI：10.1109/TITS.2022.3172241.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Approximation algorithms
Quantum computing
Vehicle routing
Routing
Qubit
Program processors
Vehicle routing problem
ising model
combinatorial optimization
quantum approximate algorithms
variational quantum algorithms
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