Scheduling elective surgery under uncertainty and downstream capacity constraints

Min, Daiki; Yih, Yuehwern

doi:10.1016/j.ejor.2010.03.014

Cited by 213 publications

(122 citation statements)

References 26 publications

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“…Model lain, dengan menggunakan pemrograman stokastik dibahas oleh Min dan Yih [6] dengan cara metode penaksiran rata-rata sampel untuk mendapatkan jadwal operasi optimal dengan tujuan meminimumkan biaya pasien dan biaya waktu lembur ruang operasi yang digunakan.…”

Section: Gambar 1 Simulasi Block Rooms Dan Open Posting Rooms (Weissunclassified

Model Optimisasi Robust untuk Mengatasi Ketidaktentuan Estimasi Durasi Operasi pada Masalah Penjadwalan Ruang Operasi Rumah Sakit

Chaerani

Royana

Hertini

2017

jti

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Abstract:The problem of operating room scheduling in hospitals is a matter of diversity in duration of operation that requires scheduling to reduce the level of busyness of the operating room. Operating room scheduling is how to put patients into optimally available operating room blocks to minimize patient waiting time. This problem is presented in integer linear programming (ILP) formulation. In this paper, a discussion on how to model the uncertain operating room scheduling problem using Robust Optimization (RO) is presented. In RO methodology, it is introduced Robust counterpart (RC) methodology, i.e., a methodology that can be used to model what so-called robust counterpart of the uncertain optimization model for operating room scheduling problem. By choosing uncertainty box sets and ellipsoidal uncertainty, the modeling results show that the obtained RC model for the uncertain operating room scheduling problems belongs to the class of computationally tractable problems, in which it is linear programming for box uncertainty set and conic quadratic programming for ellipsoidal uncertainty set with binary variables involved. In this paper, example is presented using software Maple.

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Section: Gambar 1 Simulasi Block Rooms Dan Open Posting Rooms (Weissunclassified

Model Optimisasi Robust untuk Mengatasi Ketidaktentuan Estimasi Durasi Operasi pada Masalah Penjadwalan Ruang Operasi Rumah Sakit

Chaerani

Royana

Hertini

2017

jti

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“…Guido [30] as well as Min and Yih [65] argue that a surgery schedule should consider patient priority and that inadequate consideration of patient priority may result in a suboptimal and ineffective schedule. In general, non-elective patients get priority over the patients in the elective schedule and often it is the attending staff surgeon who decides on the exact order.…”

Section: Categorization and Prioritizationmentioning

confidence: 99%

Trade-Offs in Operating Room Planning for Electives and Emergencies: A Review

Riet

Demeulemeester

2014

SSRN Journal

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The planning of the operating rooms (ORs) is a difficult process due to the different stakeholders involved. The real complexity, however, results from various sources of variability entering the processes. These uncertain processes cannot be ignored since they greatly influence the trade-offs between the hospital costs and the patient waiting times. As a result, a need for policies guiding the OR manager in handling the trade-offs arises. Therefore, researchers have investigated different possibilities to incorporate non-elective patients in the schedule with the goal of maximizing both patient-and hospital-related measures. This paper reviews the literature on OR planning where both elective and non-elective patient categories are involved. It shows the various policies, the differences and similarities in the research settings and the resulting outcomes, whether they are beneficial or not. We find that the dedicated and the flexible policy are mostly pursued, but the setting and the assumptions of the reviewed papers vary widely. Decisions on both operational policies as well as on capacity are required to assure timely access and efficiency, which are the two main drivers for the problem at hand. Furthermore, the policy choice impacts the number of schedule disruptions and the OR utilization. However, results on the overtime and the patient waiting time are partly contradicting. The review shows that some policies have already received considerable attention, but the question of which policies are most appropriate is not yet fully answered. Neither has the full spectrum of policies been explored yet. Consequently, this topic provides several areas for future research, which are outlined throughout the paper.

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“…Stochastic programming has been applied in various health care settings. [27] developed a stochastic program to include uncertainty in surgery durations and length of stay for operating room scheduling, [3] used a stochastic programming approach for nurse scheduling, and [37] applied stochastic programming to appointment scheduling. To the best of our knowledge, stochastic programming has not been applied to multi-appointment planning with uncertain patient routing before.…”

Section: Formal Problem Description and Solution Approachmentioning

confidence: 99%

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Leeftink

Vliegen

Hans

2017

Health Care Manag Sci

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Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multidisciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital's problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

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Scheduling elective surgery under uncertainty and downstream capacity constraints

Cited by 213 publications

References 26 publications

Model Optimisasi Robust untuk Mengatasi Ketidaktentuan Estimasi Durasi Operasi pada Masalah Penjadwalan Ruang Operasi Rumah Sakit

Model Optimisasi Robust untuk Mengatasi Ketidaktentuan Estimasi Durasi Operasi pada Masalah Penjadwalan Ruang Operasi Rumah Sakit

Trade-Offs in Operating Room Planning for Electives and Emergencies: A Review

Stochastic integer programming for multi-disciplinary outpatient clinic planning

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