Quantum computing-based optimization method for train short-turn routing with flexible composition
The joint optimization of train timetable and short-turn routing under the flexible composition mode are restricted by various factors such as train timetables,passenger dynamic equations,and train composition adaptability.The coupling of constraints increases the complexity of the problem,making it difficult to solve using traditional optimization methods.This paper introduces the quantum computing method to address the problem.We built a mixed-integer nonlinear programming model to minimize the number of gathered passengers across all stations along the transit line.Furthermore,we used the real coherent Ising machine(CIM)to solve this problem.The numerical results show that the real coherent Ising machine has obvious advantages in computing efficiency and optimization performance compared with other classical algorithms.
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