Abstract
In recent years, the scale of the health examination business has increased rapidly, and research on the combinatorial optimization of medical examinations has become more important. In this context, a special large-scale flexible open shop scheduling problem (FOSP) is introduced based on the idea of the multi-processor open shop scheduling problem. A mixed integer programming model is developed for the FOSP, which regards client satisfaction as the most important objective. As the FOSP is particularly complex, three different intelligent optimization algorithms are examined, namely a genetic algorithm, hybrid particle swarm optimization, and simulated annealing. According to the medical examination preferences of the clients, a group of large-scale test problems are created on the basis of benchmark instances of the flexible job shop problem, and these are used to evaluate the performance of each algorithm. The experimental results show that the genetic algorithm outperforms both simulated annealing and hybrid particle swarm optimization, especially in large-scale problems.
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Acknowledgements
This work is supported by the National Natural Science Fund for Distinguished Young Scholars of China (61525304), National Natural Science Foundation of China (Nos. 61773120, 61473301, 71501180, 71501179 and 61603400). This work is also supported in part by the Program for New Century Excellent Talents in University, and Shenzhen Basic Research Project for Development of Science and Technology (JCYJ20160530141956915).
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Zhang, J., Wang, L. & Xing, L. Large-scale medical examination scheduling technology based on intelligent optimization. J Comb Optim 37, 385–404 (2019). https://doi.org/10.1007/s10878-017-0246-6
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DOI: https://doi.org/10.1007/s10878-017-0246-6