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Optimal fuzzy scheduling and sequencing of multiple-period operating room

Published online by Cambridge University Press:  14 August 2017

Abbas Al-Refaie ,
Mays Judeh  and
Ming-Hsien Li
Abbas Al-Refaie*
Affiliation:
Department of Industrial Engineering, University of Jordan, Amman, Jordan
Mays Judeh
Affiliation:
Department of Industrial Engineering, University of Jordan, Amman, Jordan
Ming-Hsien Li
Affiliation:
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan
*
Reprint requests to: Abbas Al-Refaie, Department of Industrial Engineering, University of Jordan, Amman 11942, Jordan. E-mail: abbas.alrefai@ju.edu.jo
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Abstract

Little research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

Keywords

Fuzzy TheoryMultiple PeriodsOperating RoomSchedulingSequencing
Type
Regular Articles
Information
AI EDAM , Volume 32 , Issue 1 , February 2018 , pp. 108 - 121
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

Al-Refaie, A. (2013). Optimization of multiple responses in the Taguchi method using fuzzy regression. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(1), 99–107.CrossRefGoogle Scholar
Al-Refaie, A. (2014 a). FGP model to optimize performance of tableting process with three quality responses. Transactions of the Institute of Measurement and Control 36, 336–346.Google Scholar
Al-Refaie, A. (2014 b). Optimizing performance of low-voltage cables’ process with three quality responses using fuzzy goal programming. HKIE Transactions 21, 1–21.Google Scholar
Al-Refaie, A. (2014 c). A proposed satisfaction model to optimize process performance with multiple quality responses in the Taguchi method. Journal of Engineering Manufacture 228, 291–301.Google Scholar
Al-Refaie, A. (2015 a). Optimizing multiple quality responses in the Taguchi method using fuzzy goal programming: modeling and applications. International Journal of Intelligent Systems 30(6), 651–675.CrossRefGoogle Scholar
Al-Refaie, A. (2015 b). A proposed weighted additive model to optimize multiple quality responses in the Taguchi method with applications. Journal of Process Mechanical Engineering 229(3), 168–178.Google Scholar
Al-Refaie, A., Aldwairi, R., & Chen, T. (2017). Optimizing performance of rigid polyurethane foam using FGP models. Journal of Ambient Intelligence and Humanized Computing. Advance online publication. doi:10.1007/s12652-016-0441-9 Google Scholar
Al-Refaie, A., Chen, T., & Al-Athamneh, R. (2016). Fuzzy neural network approach to optimizing process performance by using multiple responses. Journal of Ambient Intelligence and Humanized Computing 7, 801–816.CrossRefGoogle Scholar
Al-Refaie, A., Chen, T., & Judeh, M. (2016). Optimal operating room scheduling for normal and unexpected events in a smart hospital. Operational Research. Advance online publication. doi:10.1007/s12351-016-0244-y Google Scholar
Al-Refaie, A., Diabat, A., & Li, M.-H. (2014). Optimizing tablets’ quality with multiple responses using fuzzy goal programming. Journal of Process Mechanical Engineering 228(2), 115–126.CrossRefGoogle Scholar
Al-Refaie, A., Judeh, M., & Chen, T. (2017). Optimal multiple-period scheduling and sequencing of operating room and intensive care unit. Operational Research. Advance online publication. doi:10.1007/s12351-016-0287-0 Google Scholar
Barbagallo, S., Corradi, L., de Goyet, J.V., Iannucci, M., Porro, I., Rosso, N., Tanfani, E., & Testi, A. (2015). Optimization and planning of operating theatre activities: an original definition of pathways and process modeling. BMC Medical Informatics and Decision Making 15(38), 1–16.Google Scholar
Devi, S.P., Rao, K.S., & Sangeetha, S.S. (2012). Prediction of surgery times and scheduling of operation theaters in optholmology department. Journal of Medical Systems 36, 415–430.Google Scholar
Guinet, A., & Chaabane, S. (2003). Operating theatre planning. International Journal of Production Economics 85, 69–81.Google Scholar
Lamiri, M., Xie, X., Dolgui, A., & Grimaud, F. (2008). A stochastic model for operating room planning with elective and emergency demand for surgery. European Journal of Operational Research 185, 1026–1037.Google Scholar
Nouaouri, I., Nicolas, J.-C., & Jolly, D. (2009). Scheduling of stabilization surgical cares in case of a disaster. Proc. IEEE Int. Conf. Industrial Engineering and Engineering Management (IEEM), Hong Kong, December 8–11.CrossRefGoogle Scholar
Nouaouri, I., Nicolas, J.-C., & Jolly, D. (2011). Operating room scheduling under unexpected events: the case of a disaster. Journal of Applied Operational Research 3(3), 163–176.Google Scholar
Persson, M., & Persson, J.A. (2006). Optimization modelling of hospital OR planning: analyzing strategies and problem settings. Proc. 2006 Annual Conf. OR Applied to Health Services. New York: IEEE.Google Scholar
Saadouli, H., Jerbi, B., Dammak, A., Masmoudi, L., & Bouaziz, A. (2015). A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Computers & Industrial Engineering 80, 72–79.Google Scholar
Souki, M. (2011). Operating theatre scheduling with fuzzy durations. Journal of Applied Operational Research 3(3), 177–191.Google Scholar
Testi, A., Tanfani, E., & Torre, G. (2007). A three-phase approach for operating theatre schedules. Health Care Management Science 10, 163–172.Google Scholar
Testi, A., Tanfani, E., Valente, R., Ansaldo, G.L., & Torre, G.C. (2008). Prioritizing surgical waiting lists. Journal of Evaluation in Clinical Practice 14(1), 59–64.Google Scholar