Planning and scheduling operating rooms for elective and emergency surgeries with uncertain duration

Kroer, Line Ravnskjær; Foverskov, Karoline; Vilhelmsen, Charlotte; Hansen, Aske Skouboe; Larsen, Jesper

doi:10.1016/j.orhc.2018.03.006

Cited by 48 publications

(20 citation statements)

References 31 publications

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“…Considering the uncertainty at the time of surgery, they presented a robust model for minimizing surgical time, which reduced the surgical time by considering scenarios compared to definitive scheduling methods. Kroer et al [ 21 ] examined the planning of operating rooms for a hospital in Denmark, in which each surgery must be assigned to a specific operating room, as well as surgery for a specific time in order to minimize overtime and use capacity. Nouaouri et al [ 22 ] considered the uncertainty of inpatient admission and surgery time.…”

Section: Literature Reviewmentioning

confidence: 99%

Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques

Liu

Döngül

et al. 2022

Computational Intelligence and Neuroscience

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This study presents an optimization approach for scheduling the operation room for emergency surgeries, considering the priority of surgeries. This optimization model aims to minimize the costs associated with elective and emergency surgeries and maximize the number of scheduled surgeries. In this study, surgeon assistants to perform each surgery are considered in order to achieve the goals. Since the time of each surgery varies according to the conditions of the patient, this parameter is considered as an uncertain one, and a robust optimization method is applied to deal with uncertainty. To demonstrate the effectiveness of the proposed method, a case study in one of the East Asian hospitals is presented and analyzed using GAMS software. Moreover, hybrid simulation and gray wolf optimization algorithm (GWO) have been implemented to solve the optimization model in different scenarios. The results show that increasing the risk parameters in the robust optimization model will increase the system costs. Moreover, in case of uncertainty, the solutions obtained from the GWO simulation method are on average 73.75% better than the solutions obtained from the GWO algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques

Liu

Döngül

et al. 2022

Computational Intelligence and Neuroscience

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“…Min & Yih (2010) applied sample average approximation for minimizing patient cost and expected overtime cost. Mixed integer programming and Monte Carlo simulation has been used by Zhang et al (2009) for minimizing an inpatients' cost model, and by Kroer et al (2018) for minimizing overtime work and unused OR capacity. Denton et al (2010) extended their earlier work (Denton et al, 2007) by implementing a robust counterpart.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Our optimization criterion is a multi-objective function that incorporates leveling the bed occupancy at the downstream resources, attempting to schedule as many surgeries as possible, and discouraging the increase of waiting lists. Multiple objective functions are commonly implemented in OR scheduling problems (Van Oostrum et al, 2008;Banditori et al, 2013;Xiang, 2017;Jebali & Diabat, 2017;Neyshabouri & Berg, 2017;Kroer et al, 2018). Also, some studies considered minimizing the ward occupancy variability as one of the objectives (Van Oostrum et al, 2008;Carter & Ketabi, 2013;Schneider et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Minimizing bed occupancy variance by scheduling patients under uncertainty

d’Obrenan

Ridder

Roubos³

et al. 2020

European Journal of Operational Research

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In this paper we consider the problem of scheduling patients in allocated surgery blocks in a Master Surgical Schedule. We pay attention to both the available surgery blocks and the bed occupancy in the hospital wards. More specifically, large probabilities of overtime in each surgery block are undesirable and costly, while large fluctuations in the number of used beds requires extra buffer capacity and makes the staff planning more challenging. The stochastic nature of surgery durations and length of stay on a ward hinders the use of classical techniques. Transforming the stochastic problem into a deterministic problem does not result into practically feasible solutions. In this paper we develop a technique to solve the stochastic scheduling problem, whose primary objective it to minimize variation in the necessary bed capacity, while maximizing the number of patients operated, and minimizing the maximum waiting time, and guaranteeing a small probability of overtime in surgery blocks. The method starts with solving an Integer Linear Programming (ILP) formulation of the problem, and then simulation and local search techniques are applied to guarantee small probabilities of overtime and to improve upon the ILP solution. Numerical experiments applied to a Dutch hospital show promising results.

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“…They developed a metaheuristic based on a genetic algorithm to solve the proposed integer linear programming model. Kroer et al [28] further considered emergency patients with stochastic arrivals and surgical durations to minimize overtime and proposed heuristics to solve the stochastic mixed-integer programming model. Several researchers considered the uncertainty of surgical duration due to some facts.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiple Due Time Surgical Scheduling With Truncated Learning and Deteriorating Effect

Song¹,

Zhang²,

Zhu³

2019

IEEE Access

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The surgical scheduling problem that incorporates the surgical duration, the setup time, the turnover time and the due time is studied in this paper. The actual surgical duration is affected by the normal duration, the surgical sequence, the accumulated experience of surgical teams and a controlling parameter. Besides, the surgical duration, the setup time and the turnover time are affected by the deteriorating effect, which means when the start time, the setup time or the turnover time of a surgery is postponed, the actual duration, the actual setup time or the actual turnover time will be prolonged. A schedule problem is formulated to minimize the maximum surgical tardiness. By building and analyzing the surgical scheduling model, each surgical team should operate surgeries according to a non-decreasing order of patients' normal surgical duration. Furthermore, a branch-and-bound algorithm is provided to solve the surgical teams scheduling problem. Our experimental results show the effectiveness and stability of our proposed algorithm. INDEX TERMS Deteriorating effect, surgical scheduling, truncated learning effect, various due times.

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Planning and scheduling operating rooms for elective and emergency surgeries with uncertain duration

Cited by 48 publications

References 31 publications

Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques

Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques

Minimizing bed occupancy variance by scheduling patients under uncertainty

Multiple Due Time Surgical Scheduling With Truncated Learning and Deteriorating Effect

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