A stochastic optimization model for shift scheduling in emergency departments.
Excessive waiting time in Emergency Departments (ED) can be both a cause of frustration and more importantly, a health concern for patients. Waiting time arises when the demand for work goes beyond the facility's service capacity. ED service capacity mainly depends on human resources and on beds ava...
| Publicado en: | Health Care Management Science Vol. 18; no. 3; pp. 289 - 303 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109625276&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109625276 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Sep2015 vid: 18 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109625276 NLM25270574 2013133706 10.1007/s10729-014-9300-4 NLM25270574 109625276 ppf: 289 ppct: 14 formats: tig: atl: A stochastic optimization model for shift scheduling in emergency departments. aug: au: El-Rifai, Omar Garaix, Thierry Augusto, Vincent Xie, Xiaolan sug: ab: Excessive waiting time in Emergency Departments (ED) can be both a cause of frustration and more importantly, a health concern for patients. Waiting time arises when the demand for work goes beyond the facility's service capacity. ED service capacity mainly depends on human resources and on beds available for patients. In this paper, we focus on human resources organization in an ED and seek to best balance between service quality and working conditions. More specifically, we address the personnel scheduling problem in order to optimize the shift distribution among employees and minimize the total expected patients' waiting time. The problem is also characterized by a multi-stage re-entrant service process. With an appropriate approximation of patients' waiting times, we first propose a stochastic mixed-integer programming model that is solved by a sample average approximation (SAA) approach. The resulting personnel schedules are then evaluated using a discrete-event simulation model. Numerical experiments are then performed with data from a French hospital to compare different personnel scheduling strategies. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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