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Surgical rescheduling problem with emergency patients considering participants’ dissatisfaction

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Abstract

Surgical rescheduling is necessary for adjusting initial schedules on surgery day after emergency demand is realized. While people-oriented medical service has been emphasized these years, the traditional rescheduling scheme which is only in pursuit of a great cost-related performance is no longer desirable since patients and medical staff are also highly involved in rescheduling and their preferences should not be ignored. In order to provide a satisfactory and people-centered rescheduling plan, this study considers the preferences of three involved participants (i.e. the operating room manager, medical staff, and elective patients) while designing a rescheduling plan. Based on prospect theory, we introduce three functions to evaluate three participants’ dissatisfaction about rescheduling schemes in terms of their respective preferences. Then a multi-objective rescheduling model is established with multiple resource constraints, emergency lead-time target constraints, and the objective of minimizing the dissatisfaction of three participants caused by rescheduling. A hybrid particle swarm optimization (HPSO) algorithm with two improved strategies—an initial population construction strategy and a local search strategy, is then developed to solve the proposed problem. Several numerical experiments are carried out by leveraging data reported in existing case studies in conjunction with simulated data. The results demonstrate the effectiveness of two improved strategies and show that the proposed HPSO algorithm can provide better Pareto solutions for our problem in comparison with the basic non-dominated sorting genetic algorithm.

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References

  • Al-Refaie A, Chen T, Judeh M (2018) Optimal operating room scheduling for normal and unexpected events in a smart hospital. Oper Res Int Journal 18(3):579–602

    Article  Google Scholar 

  • Arroyo JEC, Armentano VA (2005) Genetic local search for multi-objective flowshop scheduling problems. Eur J Oper Res 167(3):717–738

    Article  MathSciNet  MATH  Google Scholar 

  • Bam M, Denton BT, Van Oyen MP, Cowen ME (2017) Surgery scheduling with recovery resources. IISE Trans 49(10):942–955

    Article  Google Scholar 

  • Baucells M, Rata C (2006) A survey study of factors influencing risk-taking behavior in real-world decisions under uncertainty. Decis Anal 3(3):163–176

    Article  Google Scholar 

  • Belkhamsa M, Jarboui B, Masmoudi M (2018) Two metaheuristics for solving no-wait operating room surgery scheduling problem under various resource constraints. Comput Ind Eng 126:494–506

    Article  Google Scholar 

  • Bhuvana J, Aravindan C (2016) Memetic algorithm with preferential local search using adaptive weights for multi-objective optimization problems. Soft Comput 20(4):1365–1388

    Article  Google Scholar 

  • Bruni ME, Beraldi P, Conforti D (2015) A stochastic programming approach for operating theatre scheduling under uncertainty. IMA J Manag Math 26:99–119

    Article  MathSciNet  MATH  Google Scholar 

  • Cardoen B, Demeulemeester E, Belien J (2010) Operating room planning and scheduling: a literature review. Eur J Oper Res 201(3):921–932

    Article  MATH  Google Scholar 

  • Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43–60

    Article  MathSciNet  MATH  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm-NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  • Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195

    Article  Google Scholar 

  • Dexter F, Epstein RH (2005) Operating room efficiency and scheduling. Curr Opinion Anesthesiol 18(2):195–198

    Article  Google Scholar 

  • Dexter F, Lee JD, Dow AJ, Lubarsky DA (2007) A psychological basis for anesthesiologists’ operating room managerial decision-making on the day of surgery. Anesth Analg 105(2):430–434

    Article  Google Scholar 

  • Dhami S, Al-Nowaihi A (2007) Why do people pay taxes? Prospect theory versus expected utility theory. J Econ Behav Organ 64:171–192

    Article  Google Scholar 

  • Erdem E, Qu X, Shi J (2012) Rescheduling of elective patients upon the arrival of emergency patients. Decis Support Syst 54(1):551–563

    Article  Google Scholar 

  • Ferrand YB, Magazine MJ, Rao US (2014) Partially flexible operating rooms for elective and emergency surgeries. Decis Sci 45(5):819–847

    Article  Google Scholar 

  • Freeman NK, Melouk SH, Mittenthal J (2016) A scenario-based approach for operating theater scheduling under uncertainty. Manuf Serv Oper Manag 18(2):245–261

    Article  Google Scholar 

  • Fügener A, Schiffels S, Kolisch R (2017) Overutilization and underutilization of operating rooms: insights from behavioral health care operations management. Health Care Manag Sci 20(1):115–128

    Article  Google Scholar 

  • Guerriero F, Guido R (2011) Operational research in the management of the operating theatre: a survey. Health Care Manag Sci 14(1):89–114

    Article  Google Scholar 

  • Heydari M, Soudi A (2016) Predictive/reactive planning and scheduling of a surgical suite with emergency patient arrival. J Med Syst 40(1):1–9

    Article  Google Scholar 

  • Jebali A, Diabat A (2015) A stochastic model for operating room planning under capacity constraints. Int J Prod Res 53(24):7252–7270

    Article  Google Scholar 

  • Jung KS, Pinedo M, Sriskandarajah C, Tiwari V (2019) Scheduling elective surgeries with emergency patients at shared operating rooms. Prod Oper Manag 28(6):1407–1430

    Article  Google Scholar 

  • Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–292

    Article  MathSciNet  MATH  Google Scholar 

  • Kahneman D, Tversky A (2000) Choices, values, and frames. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Lamiri M, Xie X, Zhang S (2008) Column generation approach to operating theater planning with elective and emergency patients. IIE Trans 40(9):838–852

    Article  Google Scholar 

  • Latorre-Núñez G, Lüer-Villagra A, Marianov V, Obreque C, Ramis F, Neriz L (2016) Scheduling operating rooms with consideration of all resources, post anesthesia beds and emergency surgeries. Comput Ind Eng 97:248–257

    Article  Google Scholar 

  • Liu F, Wang S, Hong Y, Yue X (2017) On the robust and stable flowshop scheduling under stochastic and dynamic disruptions. IEEE Trans Eng Manag 64(4):539–553

    Article  Google Scholar 

  • Liu L, Wang C, Wang J (2019) A combinatorial auction mechanism for surgical scheduling considering surgeon’s private availability information. J Comb Optim 37(1):405–417

    Article  MathSciNet  MATH  Google Scholar 

  • May JH, Spangler WE, Strum DP, Vargas LG (2011) The surgical scheduling problem: current research and future opportunities. Prod Oper Manag 20(3):392–405

    Article  Google Scholar 

  • Mehta A, Efron DT, Canner JK, Manukyan MC, Dultz L, Burns C, Sakran JV (2018) Surgeon variation in operating times and charges for emergency general surgery. J Surg Res 227:101–111

    Article  Google Scholar 

  • Meskens N, Duvivier D, Hanset A (2013) Multi-objective operating room scheduling considering desiderata of the surgical team. Decis Support Syst 55(2):650–659

    Article  Google Scholar 

  • Molina-Pariente JM, Hans EW, Framinan JM (2018) A stochastic approach for solving the operating room scheduling problem. Flex Serv Manuf J 30:224–251

    Article  Google Scholar 

  • Penn ML, Potts CN, Harper PR (2017) Multiple criteria mixed-integer programming for incorporating multiple factors into the development of master operating theatre timetables. Eur J Oper Res 262(1):194–206

    Article  MathSciNet  MATH  Google Scholar 

  • Pham D, Klinkert A (2008) Surgical case scheduling as a generalized job shop scheduling problem. Eur J Oper Res 185(3):1011–1025

    Article  MathSciNet  MATH  Google Scholar 

  • Rachuba S, Werners B (2014) A robust approach for scheduling in hospitals using multiple objectives. J Oper Res Soc 65(4):546–556

    Article  Google Scholar 

  • Roshanaei V, Luong C, Aleman DM, Urbach DR (2017) Collaborative operating room planning and scheduling. INFORMS J Comput 29(3):558–580

    Article  MathSciNet  MATH  Google Scholar 

  • Sheikh S, Komaki GM, Kayvanfar V (2018) Multi objective two-stage assembly flow shop with release time. Comput Ind Eng 124:276–292

    Article  Google Scholar 

  • Shen X, Yao X (2015) Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems. Inf Sci 298:198–224

    Article  MathSciNet  Google Scholar 

  • Silva TAO, De Souza MC, Saldanha RR, Burke EK (2015) Surgical scheduling with simultaneous employment of specialised human resources. Eur J Oper Res 245(3):719–730

    Article  MathSciNet  MATH  Google Scholar 

  • Stepaniak PS, Mannaerts GH, de Quelerij M, de Vries G (2009) The effect of the operating room coordinator’s risk appreciation on operating room efficiency. Anesth Analg 108(4):1249–1256

    Article  Google Scholar 

  • Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323

    Article  MATH  Google Scholar 

  • Van Essen JT, Bosch JM, Hans EW, van Houdenhoven M, Hurink JL (2014) Reducing the number of required beds by rearranging the OR-schedule. Or Spectrum 36(3):585–605

    MathSciNet  MATH  Google Scholar 

  • Wang T, Meskens N, Duvivier D (2015) Scheduling operating theatres: mixed integer programming vs. Constraint programming. Eur J Oper Res 2(247):401–413

    Article  MathSciNet  MATH  Google Scholar 

  • Wang J, Guo H, Bakker M, Tsui K (2018) An integrated approach for surgery scheduling under uncertainty. Comput Ind Eng 118:1–8

    Article  Google Scholar 

  • Wang DJ, Qiu HX, Wu CC, Lin WC, Lai KJ, Cheng SR (2019) Dominance rule and opposition-based particle swarm optimization for two-stage assembly scheduling with time cumulated learning effect. Soft Comput 23(19):9617–9628

    Article  Google Scholar 

  • Xiang W, Yin J, Lim G (2015) An ant colony optimization approach for solving an operating room surgery scheduling problem. Comput Ind Eng 85:335–345

    Article  Google Scholar 

  • Zhang X, Tian Y, Cheng R, Jin Y (2015) An efficient approach to non-dominated sorting for evolutionary multi-objective optimization. IEEE Trans Evol Comput 19(2):201–213

    Article  Google Scholar 

  • Zhu S, Fan W, Yang S, Pei J, Pardalos PM (2019) Operating room planning and surgical case scheduling: a review of literature. J Comb Optim 37(3):757–805

    Article  MathSciNet  MATH  Google Scholar 

  • Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

Download references

Acknowledgements

We thank the editor-in-chief, Professor Raffaele Cerulli, for his precious time, and the two anonymous reviewers for their valuable comments and suggestions. This research was supported by the National Natural Science Foundation of China (71672019, 71903020), the Fundamental Research Funds for the Central Universities (DUT21RW406).

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Authors and Affiliations

  1. School of Economics and Management, Dalian University of Technology, Dalian, 116024, China

    Jian-Jun Wang, Hongru Miao & Ran Xu

  2. Robinson College of Business, Georgia State University, Atlanta, GA, 30303, USA

    Jian-Jun Wang

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  1. Jian-Jun Wang
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  2. Hongru Miao
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  3. Ran Xu
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Contributions

J-JW contributed to conceptualization, funding acquisition, project administration, resources, supervision, and writing—review and editing. HM contributed to investigation, methodology, visualization, and writing—original draft and editing. RX contributed to formal analysis, methodology, and writing—original draft.

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Correspondence to Jian-Jun Wang.

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Wang, JJ., Miao, H. & Xu, R. Surgical rescheduling problem with emergency patients considering participants’ dissatisfaction. Soft Comput 25, 10749–10769 (2021). https://doi.org/10.1007/s00500-021-06014-7

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