Robust Scheduling and Planning of Operating Rooms and Sterilization Unit with Emergency and Elective Patients: Two Metaheuristic Algorithms
محورهای موضوعی : Mathematical Optimization
Fatemeh Arjmandi
1
(Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.)
Parvaneh Samouei
2
(shahid ahmadi roushan Blvd.)
کلید واژه: Operating and Sterile Rooms, Planning and Scheduling, Emergency and Elective Patients, Robust Optimization, Metaheuristic algorithms.,
چکیده مقاله :
Great attention should be paid to planning and scheduling surgeries in the operating room which is the most sensitive ward in the health context in terms of cost and specific sensitivity due to its association with the life and death of individuals. In this case, reusable sterile equipment and devices are crucial issues because the hospital or nosocomial infections result from insufficient sterilization of these instruments. Therefore, sterilization of reusable medical devices is a necessity in the operating room to prevent possible infections. This study solves the integrated operating rooms and sterile section planning problem to minimize the total costs of sterilization, surgery postponement, and performance. This study also minimizes the completion time of surgery considering nondeterministic operating times and emergency-elective patients. In the real world, surgery time may be nondeterministic based on the conditions of the patient, surgeon, equipment, and instruments; hence, it is valuable to find a robust solution for planning under such circumstances. After presenting a bi-objective mathematical model for this problem, an improved epsilon constraint method was used to solve problems with small dimensions, and two metaheuristics NSGA-II and NRGA were developed for large dimensions regarding NP-hard problems. These two algorithms were analysed in terms of five indicators. The results indicated the superiority of the NSGA-II algorithm over NRGA to solve such problems.
Great attention should be paid to planning and scheduling surgeries in the operating room which is the most sensitive ward in the health context in terms of cost and specific sensitivity due to its association with the life and death of individuals. In this case, reusable sterile equipment and devices are crucial issues because the hospital or nosocomial infections result from insufficient sterilization of these instruments. Therefore, sterilization of reusable medical devices is a necessity in the operating room to prevent possible infections. This study solves the integrated operating rooms and sterile section planning problem to minimize the total costs of sterilization, surgery postponement, and performance. This study also minimizes the completion time of surgery considering nondeterministic operating times and emergency-elective patients. In the real world, surgery time may be nondeterministic based on the conditions of the patient, surgeon, equipment, and instruments; hence, it is valuable to find a robust solution for planning under such circumstances. After presenting a bi-objective mathematical model for this problem, an improved epsilon constraint method was used to solve problems with small dimensions, and two metaheuristics NSGA-II and NRGA were developed for large dimensions regarding NP-hard problems. These two algorithms were analysed in terms of five indicators. The results indicated the superiority of the NSGA-II algorithm over NRGA to solve such problems.
[1] van Essen J T Hans E W Hurink J L and Oversberg A 2012 Minimizing the waiting time for emergency surgery. Operations research for health care. 1(2-3): 34-44. https://doi.org/10.1016/j.orhc.2012.05.002
[2] Belkhamsa M Jarboui B Masmoudi M 2018 Two metaheuristics for solving no-wait operating room surgery scheduling problem under various resource constraints. Computers & Industrial Engineering. 126: 494-506. https://doi.org/10.1016/j.cie.2018.10.017
[3] Maghzi P Roohnavazfar M Mohammadi M and Naderi B 2019 Modeling the Problem of Operating Rooms and Different Wards of Health Centers Scheduling and Planning. In 2019 15th Iran International Industrial Engineering Conference (IIIEC). https://doi.org/10.1109/IIIEC.2019.8720739
[4] Kayvanfar V Akbari Jokar M R Rafiee M Sheikh S and Iranzad R 2021 A new model for operating room scheduling with elective patient strategy. INFOR: Information Systems and Operational Research. 59(2): 309-332. https://doi.org/10.1080/03155986.2021.1881359
[5] Ali H H Lamsali H and Othman S N 2019 Operating rooms scheduling for elective surgeries in a hospital affected by war-related incidents. Journal of medical systems. 43(5): 1-9. https://doi.org/10.1007/s10916-019-1263-z
[6] Kamran M A Karimi B Dellaert N and Demeulemeester E 2019 Adaptive operating rooms planning and scheduling: A rolling horizon approach. Operations Research for Health Care. 22: 100200. https://doi.org/10.1016/j.orhc.2019.100200
[7] Bovim T R Christiansen M Gullhav A N Range T M and Hellemo L 2020 Stochastic master surgery scheduling. European Journal of Operational Research. 285(2): 695-711. https://doi.org/10.1016/j.ejor.2020.02.001
[8] Spagnolo A M Ottria G Amicizia D Perdelli F and Cristina M L 2013 Operating theatre quality and prevention of surgical site infections. Journal of preventive medicine and hygiene. 54(3): 131. https://doi.org/10.15167/2421-4248/JPMH2013.54.3.398
[9] Beroule B Grunder O Barakat O Aujoulat O and Lustig H 2016 Operating room scheduling including medical devices sterilization: towards a transverse logistic. IFAC-PapersOnLine. 49(12): 1146-1151. https://doi.org/10.1016/j.ifacol.2016.07.657
[10] Ozturk O Di Mascolo M Espinouse M L and Gouin A 2010 Schedulling of washing operations in a hospital sterilization service for minimizing the mean predisinfection excess time of medical devices. Technical Report, G-SCOP.
[11] Ozturk O 2020 A bi-criteria optimization model for medical device sterilization. Annals of Operations Research. 293(2): 809-831. https://doi.org/10.1007/s10479-019-03296-x
[12] Bruni M E Beraldi P and Conforti D 2015 A stochastic programming approach for operating theatre scheduling under uncertainty. IMA Journal of Management Mathematics. 26(1): 99-119. https://doi.org/10.1093/imaman/dpt027
[13] Eun J Kim S P Yih Y and Tiwari V 2019 Scheduling elective surgery patients considering time-dependent health urgency: Modeling and solution approaches. Omega. 86: 137-153. https://doi.org/10.1016/j.omega.2018.07.007
[14] Kroer L R Foverskov K Vilhelmsen C Hansen A S and Larsen J 2018 Planning and scheduling operating rooms for elective and emergency surgeries with uncertain duration. Operations research for health care. 19: 107-119. https://doi.org/10.1016/j.orhc.2018.03.006
[15] M'Hallah R and Visintin F 2019 A stochastic model for scheduling elective surgeries in a cyclic master surgical schedule. Computers & Industrial Engineering. 129: 156-168. https://doi.org/10.1016/j.cie.2019.01.030
[16] Kamran M A Karimi B and Dellaert N 2020 A column-generation-heuristic-based benders’ decomposition for solving adaptive allocation scheduling of patients in operating rooms. Computers & Industrial Engineering. 148: 106698. https://doi.org/10.1016/j.cie.2020.106698
[17] Rachuba S and Werners B 2017 A fuzzy multi-criteria approach for robust operating room schedules. Annals of Operations Research. 251(1-2): 325-350. https://doi.org/10.1007/s10479-015-1926-1
[18] Liu C Wang J and Liu M 2017 A scenario-based robust optimization approach for surgeries scheduling with a single specialised human resource server. In 2017 International Conference on Service Systems and Service Management IEEE.https://doi.org/10.1016/10.1109/ICSSSM.2017.7996260
[19] Aissaoui N O Khlif H H and Zeghal F M 2020 Integrated proactive surgery scheduling in private healthcare facilities. Computers & Industrial Engineering. 148: 106686. https://doi.org/10.1016/j.cie.2020.106686
[20] Coban E 2020 The effect of multiple operating room scheduling on the sterilization schedule of reusable medical devices. Computers & Industrial Engineering. 147: 106618. https://doi.org/10.1016/j.cie.2020.106618
[21] Mazloumian M Baki M F and Ahmadi M 2022 A robust multiobjective integrated master surgery schedule and surgical case assignment model at a publicly funded hospital. Computers & Industrial Engineering. 163: 107826. https://doi.org/10.1016/j.cie.2021.107826
[22] Harris S and Claudio D 2022 Current Trends in Operating Room Scheduling 2015 to 2020: a Literature Review. Operations Research Forum. 3(1): 1-42. https://doi.org/10.1007/s43069-022-00134-y
[23] Wang J J Dai Z Chang A C and Shi J J 2022 Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations. Annals of Operations Research. 1-43. https://doi.org/10.1007/s10479-022-04645-z
[24] Yu H Li J Q Chen X L Niu W and Sang H Y 2022 An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times. Cluster Computing. 1-26. https://doi.org/10.1007/s10586-022-03589-0
[25] Ghasemi S Tavakkoli-Moghaddam R and Hamid M 2023 Operating room scheduling by emphasising human factors and dynamic decision-making styles: a constraint programming method. International Journal of Systems Science: Operations & Logistics. 10(1): 2224509. https://doi.org/10.1080/23302674.2023.2224509
[26] Bargetto R Garaix T and Xie X 2023 A branch-and-price-and-cut algorithm for operating room scheduling under human resource constraints. Computers & Operations Research. 152: 106136. https://doi.org/10.1016/j.cor.2022.106136
[27] Arjmandi F and Samouei P 2023 Multi-objective planning and scheduling of operating rooms and sterile section reusable surgical devices with a scenario-based robust optimization approach. Industrial Management Studies, 21(70), https://doi.org/10.22054/jims.2023.66215.2762 (In Persian).
[28] Xu S and Wang J 2018 An Efficient Batch Scheduling Model for Hospital Sterilization Services Using Genetic Algorithm. International Journal of Strategic Decision Sciences (IJSDS). 9(1): 1-17. https://doi.org/10.4018/IJSDS.2018010101
[29] Mulvey J M and Ruszczyński A 1995 A new scenario decomposition method for large-scale stochastic optimization. Operations research. 43(3): 477-490. https://doi.org/10.1287/opre.43.3.477
[30] Deb K Pratap A Agarwal S and Meyarivan, T A M T 2002 A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation. 6(2): 182-197. https://doi.org/10.1109/4235.996017
[31] Al Jadaan O Rajamani L and Rao C R 2008 Non-dominated ranked genetic algorithm for solving multiobjective optimization problems in NRGA. Journal of Theoretical and Applied Information Technology. 60--67. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.9427
[32] Rahmati S H A Zandieh M and Yazdani M 2013 Developing two multi-objective evolutionary algorithms for the multiobjective flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology. 64(5): 915-932. https://doi.org/10.1007/s00170-012-4051-1
[33] Kord H and Samouei P 2023 Coordination of humanitarian logistic based on the quantity flexibility contract and buying in the spot market under demand uncertainty using NSGA-II and NRGA algorithms. Expert Systems with Applications. 214: 119187. https://doi.org/10.1016/j.eswa.2022.119187
[34] Heidari A Imani D M and Khalilzadeh M 2020 A hub location model in the sustainable supply chain considering customer segmentation. Journal of Engineering, Design and Technology 19(6):1387-1420. https://doi.org/10.1108/JEDT-07-2020-0279
[35] Hajipour V Fattahi P Tavana M and Di Caprio D 2016 Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics. Applied Mathematical Modelling. 40(7-8): 4948-4969. https://doi.org/10.1016/j.apm.2015.12.013
[36] Janatyan N Zandieh M Alem Tabriz A and Rabieh M 2019 Optimizing Sustainable Pharmaceutical Distribution Network Model with Evolutionary Multi-objective Algorithms (Case Study: Darupakhsh Company). Journal of Production and Operations Management. 10(1): 133-153. https://doi.org/10.22108/jpom.2019.110116.1123
[37] Hecke T V 2012 Power study of anova versus Kruskal-Wallis test. Journal of Statistics and Management Systems. 15(2-3): 241-247.https://doi.org/10.1080/09720510.2012.10701623
