An imperialist competition algorithm using a global search strategy for physical examination scheduling
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An imperialist competition algorithm using a global search strategy for physical examination scheduling Hui Yu1 · Jun-qing Li1,2
· Lijing Zhang3 · Peng Duan2
Accepted: 24 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The outbreak of the novel coronavirus clearly highlights the importance of the need of effective physical examination scheduling. As treatment times for patients are uncertain, this remains a strongly NP-hard problem. Therefore, we introduce a complex flexible job shop scheduling model. In the process of physical examination for suspected patients, the physical examiner is considered a job, and the physical examination item and equipment correspond to an operation and a machine, respectively. We incorporate the processing time of the patient during the physical examination, the transportation time between equipment, and the setup time of the patient. A unique scheduling algorithm, called imperialist competition algorithm with global search strategy (ICA GS) is developed for solving the physical examination scheduling problem. A local search strategy is embedded into ICA GS for enhancing the searching behaviors, and a global search strategy is investigated to prevent falling into local optimality. Finally, the proposed algorithm is tested by simulating the execution of the physical examination scheduling processes, which verify that the proposed algorithm can better solve the physical examination scheduling problem. Keywords Flexible job shop scheduling · Physical examination scheduling · Transportation time · Setup time · Imperialist competition algorithm · Local search · Global search
1 Introduction The problem studied here is a result of the novel coronavirus outbreak that began in 2019, which first attacked Wuhan, China, and quickly spread to most regions of the country. Three months later, the virus had swept the world, and as of April 28, 2020, the novel coronavirus epidemic infected 3034801 people and killed 210511 (according to data released by Johns Hopkins University in the United States https://gisanddata.maps.arcgis.com/apps/opsdashboard/ index.html#/bda7594740fd40299423467b48e9ecf6). Facing so many patients within a short period became a critical challenge for hospitals with limited resources and
Jun-qing Li
[email protected] 1
School of Information Science and Engineering, Shandong Normal University, Jinan, China
2
School of Computer, Liaocheng University, Liaocheng, China
3
Liaocheng Jingxin Seamless Pipe Manufacturing Co., Ltd, Liaocheng, China
equipment [1, 2]. Using available resources with the best efficiency, improving patient flow, and optimizing treatment management are crucial for hospitals [3]. With the growth of the disease, many hospitals expanded capacity because the number of patients, even in Wuhan, far exceeded the standard carrying capacity. However, due to many obstacles, these expansion activities encountered many restrictions, which further reduced the available resources for meeting established needs effic
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