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.
Combinatorial optimization Flexible open shop scheduling problem Multi-processor open shop scheduling problem Mixed integer programming model Large-scale Intelligent optimization algorithms
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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).
Abdelmaguid TF (2014) A hybrid PSO-TS approach for proportionate multiprocessor open shop scheduling. In: 2014 IEEE international conference on industrial engineering and engineering management, pp 107–111. https://doi.org/10.1109/IEEM.2014.7058610
Chen H, Ihlow J, Lehmann C (1999) A genetic algorithm for flexible job-shop scheduling. In: IEEE international conference on robotics and automation, 1999. Proceedings, vol 2, pp 1120–1125Google Scholar
Goldansaz SM, Jolai F, Anaraki AHZ (2013) A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Appl Math Model 37(23):9603–9616MathSciNetCrossRefzbMATHGoogle Scholar
Hasan SMK, Sarker R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memet Comput 1(1):69–83CrossRefGoogle Scholar
Hu J, Jiang Y, Zhou P, Zhang A, Zhang Q (2017) Total completion time minimization in online hierarchical scheduling of unit-size jobs. J Comb Optim 33(3):866–881MathSciNetCrossRefzbMATHGoogle Scholar
Naderi B, Fatemi Ghomi SMT, Aminnayeri M, Zandieh M (2011) Scheduling open shops with parallel machines to minimize total completion time. J Comput Appl Math 235(5):1275–1287MathSciNetCrossRefzbMATHGoogle Scholar
Nagamani M, Chandrasekaran E (2015) Single objective for partial flexible open shop scheduling problem using hybrid particle swarm optimization algorithms. Indian J Sci Technol 8(35):1–6Google Scholar
Wang D, Liu F, Yin Y, Wang J, Wang Y (2015) Prioritized surgery scheduling in face of surgeon tiredness and fixed off-duty period. J Comb Optim 30(4):967–981MathSciNetCrossRefzbMATHGoogle Scholar
Witkowski T, Antczak P, Antczak A (2011) Hybrid method for solving flexible open shop scheduling problem with simulated annealing algorithm and multi-agent approach. Adv Mater Res 383–390:4612–4619CrossRefGoogle Scholar