A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints

Xiang, Wei; Yin, Jiao; Lim, Gino J.

doi:10.1016/j.artmed.2014.12.005

Cited by 55 publications

(22 citation statements)

References 35 publications

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“…Wang and Fung (2015) proposed a Markov decision process for scheduling sequential appointments to maximize patient satisfaction level and, dynamic programming for avoiding the curse of dimensionality. Xiang et al (2015) introduced a modified ACO algorithm with a two-level ant graph model to solve the surgery-scheduling problem. They also integrated the surgery scheduling, nurse scheduling and, systematic optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Using a metaheuristic algorithm for solving a home health care routing and scheduling problem

Manavizadeh¹,

Farrokhi-Asl²,

Beiraghdar³

2020

10.5267/j.jpm

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The Health Care system is changing from the hospitalization to the home care, and the World Health Organization has announced that the rate of care-dependent elderly people in Europe will considerably increase within the next decades. Thus, scientific planning for this area is an essential factor to improve the community health. This paper aims to develop a mathematical modeling for Home Health Care Routing and Scheduling Problem and to solve it by means of Simulated Annealing (SA) algorithm considering real condition (staff vehicle traveling, conditions of patients and so forth). We permit interdependent services for patients in which they can order as many services as they want with any relation between them (Multiple Services) and supposed time window for each service. The mathematical formulation of the problem is coded in GMAS software, which is a well-known commercial software for solving optimization problems. In addition, for large-scale problems where GAMS is unable to solve, SA algorithm is applied to tackle the problems. Finally, sensitivity analysis on the most important parameters (number of services and number of patients with interdependent Multiple services) are conducted. The results reveal that when each patient can order infinite services with any relation between them, complexity of the problem increases, but SA algorithm can solve large instances with reasonable solution in the less computational time. Thus, SA algorithm shows a rational performance for large instances. Moreover, the most important factors that affect the objective value and the run time of the problems are number of patients, and number of patients with interdependent multiple services.

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Section: Literature Reviewmentioning

confidence: 99%

Using a metaheuristic algorithm for solving a home health care routing and scheduling problem

Manavizadeh¹,

Farrokhi-Asl²,

Beiraghdar³

2020

10.5267/j.jpm

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“…Focusing on the short-term operational level of OT planning, we have found a range of different approaches and problem structures. Studies can mainly be categorized into considering completely deterministic "off-line" problems [4,6,12,30,33], to incorporating uncertainty features such as random procedure time [1,9,17] and disruptions caused by emergency demand [11,17]. Surprisingly, we only encountered two studies on the allocation of patients where stochastic future arrivals were accounted for [23,34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The specific modeling approaches of short-term OT planning range from mathematical programming and heuristics [1,4,9,11,12,30,33] to Discrete-event and Monte Carlo simulation [10,27], and further to a mixture of these [17]. For the purely deterministic cases, Xiang et al [33] and Van Huele and Vanhoucke [30] combine the surgical scheduling problem with a staff rostering problem. Xiang et al [33] develop a modified Ant Colony Optimization algorithm and test the model by using both data from the literature and real data from a Chinese hospital.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the purely deterministic cases, Xiang et al [33] and Van Huele and Vanhoucke [30] combine the surgical scheduling problem with a staff rostering problem. Xiang et al [33] develop a modified Ant Colony Optimization algorithm and test the model by using both data from the literature and real data from a Chinese hospital. Van Huele and Vanhoucke [30] approach the problem by using Mixed Integer Linear Programming (MILP) based on the most frequent objectives and constraints from the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

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Simulation-Based Rolling Horizon Scheduling for Operating Theatres

Andersen

Stidsen

Reinhardt

2020

SN Oper. Res. Forum

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Daily scheduling of surgical operations is a complicated and recurrent problem in the literature on health care optimization. In this study, we present an often overlooked approach to this problem that incorporates a rolling and overlapping planning horizon. The basis of our modeling approach is a Markov decision process, where patients are scheduled to a date and room on a daily basis. Acknowledging that both state and action space are only partially observable, we employ our model using a simulation-based method, where actions are derived from a heuristic search procedure.We test the potential of using this modeling approach on the resulting hospital costs and number of patients that are outsourced to avoid violating constraints on capacity. Using data from a Danish hospital, we find a distinct improvement in performance when compared with a policy that resembles a manual planner. Further analysis shows that substantial improvements can be attained by employing other simple policies.

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“…Por ejemplo, Xiang et al (14) midieron la efectividad de un algoritmo de optimización en la programación de diez salas de cirugías, a partir de una muestra de diez días de trabajo y alrededor de 450 cirugías. En este estudio, al igual que lo propuesto en Saadouli et al (15), se tomó una muestra de 31 días de trabajo del servicio de cirugía de un hospital privado, compuesto por diez salas para procedimientos electivos y una sala dedicada a la atención de urgencias.…”

Section: Discusión Y Conclusionesunclassified

Control de la variabilidad en la programación de pacientes electivos en salas de cirugía

Díaz-López

Fuquen-Fraile

Barrera

et al. 2015

RGYPS

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El Departamento de Cirugía genera una buena parte de los ingresos de un hospital. Este estudio evalúa la variabilidad en la programación de las cirugías y cómo esta atenta contra la eficiencia del servicio. Este estudio implementó un algoritmo para la generación de dos programaciones que luego fueron comparadas con la realizada manualmente en un hospital en Colombia. Las dos programaciones generadas se diferencian entre sí al incluir o no las máximas desviaciones en los tiempos de duración de las cirugías, asumiendo que estas son causadas por factores externas al proceso y pueden ser eliminadas. Los resultados muestran que al emplear un algoritmo automático para programar las cirugías, podrían mejorarse el tiempo de respuesta y la utilización de las salas. Aún más, se pudo concluir que la variabilidad asociada a la programación manual tiene un mayor impacto que la asociada a otros factores en los indicadores de desempeño del servicio.

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A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints

Cited by 55 publications

References 35 publications

Using a metaheuristic algorithm for solving a home health care routing and scheduling problem

Using a metaheuristic algorithm for solving a home health care routing and scheduling problem

Simulation-Based Rolling Horizon Scheduling for Operating Theatres

Control de la variabilidad en la programación de pacientes electivos en salas de cirugía

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