caa a22 4500 605469806 CHVBK 20210128100324.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00500-014-1453-z doi (NATIONALLICENCE)springer-10.1007/s00500-014-1453-z Particle swarm optimization-based planning and scheduling for a laminar-flow operating room with downstream resources [Elektronische Daten] [Yu Wang, Jiafu Tang, Zhendong Pan, Chongjun Yan] This paper focuses on finding a satisfactory surgery scheduling to patients and efficiently managing scarce medical resources in laminar-flow operating theaters, which have distinct flow process and characteristics relative to general operating theaters and are widely used in China. The problem is solved in two phases. The first phase involves determining whether patients can be operated upon within the planning period and, if so, determining their surgery date with the objective of maximizing overall patient satisfaction. In the second phase, the surgery schedule consists of the surgery sequence and the corresponding operating room; with regard to the post-anesthesia care unit, a downstream resource, the daily scheduling problem is modeled as a two-stage no-wait hybrid flow-shop problem with the objective of minimizing the hospital's operating costs, which includes the fixed costs of opening operating rooms, overtime costs and recovery costs. A discrete particle swarm optimization algorithm combined with heuristic rules is proposed. The results of experiments of our approach with realistic data show that our algorithm produced results of nearly the same quality as CPLEX but with a much less computation time. This approach can find the optimal number of daily functional operating rooms and recovery beds by varying parameters in the experiment, which gives management insights into reducing the operating costs of the hospital. Springer-Verlag Berlin Heidelberg, 2014 Operating room nationallicence Scheduling surgical cases nationallicence Particle swarm optimization nationallicence No-wait hybrid flow shop nationallicence Wang Yu Department of Systems Engineering, The State Key Lab of Synthetic Automation of Process Industry, Northeastern University, 110004, Shenyang, People's Republic of China aut Tang Jiafu College of Management Science and Engineering, Dongbei University of Finance and Economics, 116025, Dalian, People's Republic of China aut Pan Zhendong College of Management Science and Engineering, Dongbei University of Finance and Economics, 116025, Dalian, People's Republic of China aut Yan Chongjun College of Management Science and Engineering, Dongbei University of Finance and Economics, 116025, Dalian, People's Republic of China aut Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2913-2926 1432-7643 19:10<2913 2015 19 500 https://doi.org/10.1007/s00500-014-1453-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1453-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Yu Department of Systems Engineering, The State Key Lab of Synthetic Automation of Process Industry, Northeastern University, 110004, Shenyang, People's Republic of China aut NATIONALLICENCE 700 1- Tang Jiafu College of Management Science and Engineering, Dongbei University of Finance and Economics, 116025, Dalian, People's Republic of China aut NATIONALLICENCE 700 1- Pan Zhendong College of Management Science and Engineering, Dongbei University of Finance and Economics, 116025, Dalian, People's Republic of China aut NATIONALLICENCE 700 1- Yan Chongjun College of Management Science and Engineering, Dongbei University of Finance and Economics, 116025, Dalian, People's Republic of China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2913-2926 1432-7643 19:10<2913 2015 19 500