Optimization methods for a stochastic surgery planning problem

Lamiri, Mehdi; Grimaud, Frédéric; Xie, Xiaolan

doi:10.1016/j.ijpe.2008.11.021

Cited by 109 publications

(74 citation statements)

References 28 publications

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“…This case mix gives the opportunity to derive high performances on different performance indicators, for example in terms of utilization, compared to case mixes with higher coefficients of variations or longer surgery durations (Tyler et al 2003). Lamiri et al (2009) developed methods for operating room planning with shared capacity. Their instances were based on surgeries with a uniform distributed duration on the interval [0.5, 3.0].…”

Section: Example Application Of Case MIX Classificationmentioning

confidence: 99%

Case mix classification and a benchmark set for surgery scheduling

Leeftink

Hans

2017

J Sched

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Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of "instance proximity" to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible.

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Section: Example Application Of Case MIX Classificationmentioning

confidence: 99%

Case mix classification and a benchmark set for surgery scheduling

Leeftink

Hans

2017

J Sched

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“…Emergency cases may play a significant role during the execution of the surgical case schedule [224]. Hence, incorporating knowledge about emergency cases, for example predicted demand, in surgical case scheduling decreases staff overtime and patient waiting time [57,196,306,307,308]. Often, surgical case scheduling is done in isolation.…”

Section: Methods: No Articles Foundmentioning

confidence: 99%

“…Methods: computer simulation [10,57,102,137,140,142,143,146,170,226,300,302,306,307,433,470,511], heuristics [10,13,83,131,136,175,177,225,226,256,306,308,341,416,422,455,486], Markov processes [196,228,365,381], mathematical programming [13,23,81,82,83,95,102,129,130,131,175,176,177,…”

Section: Methods: No Articles Foundmentioning

confidence: 99%

“…Staff overtime is the result when the reserved capacity is insufficient to serve all arriving emergency patients, but resource idle time increases when too much capacity is reserved, causing growth in elective waiting lists [57,307,308,396,536]. Capacity can be reserved by dedicating one or more operating rooms to emergency cases, or by reserving capacity in elective operating rooms [84,306,444].…”

Section: Tactical Planningmentioning

confidence: 99%

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Integrated decision making in healthcare : an operations research and management science perspective

Hulshof¹

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“…Herein the objective function of a stochastic problem is approximated using a set of generated scenarios. [155,156] use SAA to model the operating room planning problem in this manner, however they assume fixed surgery times. They find that by incorporating the stochastic aspect of emergency arrivals, costs may be significantly reduced.…”

Section: Literaturementioning

confidence: 99%

Managing urgent care in hospitals

Borgman¹

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Optimization methods for a stochastic surgery planning problem

Cited by 109 publications

References 28 publications

Case mix classification and a benchmark set for surgery scheduling

Case mix classification and a benchmark set for surgery scheduling

Integrated decision making in healthcare : an operations research and management science perspective

Managing urgent care in hospitals

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