Objectives Due to the randomness and uncertainty of city medical rescue vehicles, the scheduling of vehicles need to be dynamically optimized. Based on time window constraints and big data technology, we propose a method to reduce the running time of emergency vehicles and improve punctuality.
Methods Big data technology is used to implement unified management and classify the heterogeneous distributed data. The symbolic method is then introduced for encoding and decoding based on the greedy method. In order to improve the convergence, the adaptive mechanism of quantum crossover probability and mutation probability is proposed.
Results Experiment contrast analysis shows that the quantum genetic algorithm can quickly converge to the optimal solution compared with other existing intelligent algorithms. Big data technology can manage and analyze complex traffic conditions, which helps to improve emergency vehicle routing, save time, improve journey accuracy and ensure time delays do not affect timely treatment.
Conclusions This method can meet the real-time requirements of the dynamic scheduling of medical rescue vehicles using the quantum genetic algorithm and big data technology. It can solve the problem of dynamic scheduling of medical rescue vehicles effectively.
Acknowledgments This research was financially supported by Liaoning Social Planning Fund, China (Grant No. L16BGL008), National Natural Science Foundation, China (Grant No. 51579024), and Dr scientific research fund of Liaoning Province (Grant No. 201601244).
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