A multi-objective approach to nurse scheduling with both hard and soft constraints

Berrada, Ilham; Ferland, Jacques A.; Michelon, Philippe

doi:10.1016/0038-0121(96)00010-9

Cited by 140 publications

(85 citation statements)

References 13 publications

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“…8,9,10,11 Several studies have employed optimization methods to solve the NSP. Some authors use exact methods like linear, integer or mixed integer programming, 12 goal programming 13,14 or constraint programming. 15,16 Many recent papers tackle the NSP with metaheuristic methods such as genetic algorithms, 17 tabu search 13,7,17 or simulated annealing.…”

Section: Related Workmentioning

confidence: 99%

“…Some authors use exact methods like linear, integer or mixed integer programming, 12 goal programming 13,14 or constraint programming. 15,16 Many recent papers tackle the NSP with metaheuristic methods such as genetic algorithms, 17 tabu search 13,7,17 or simulated annealing. 18 Vanhoucke and Maenhout 11 respond to the need for a benchmark database (highlighted in the work of Burke et al 9 and Cheang et al 8 ), to make the algorithm comparison easier.…”

Section: Related Workmentioning

confidence: 99%

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Anesthesiology Nurse Scheduling using Particle Swarm Optimization

Altamirano¹,

Riff²,

Araya³

et al. 2012

IJCIS

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In this article we present an approach designed to solve a real world problem: the Anesthesiology Nurse Scheduling Problem (ANSP) at a public French hospital. The anesthesiology nurses are one of the most shared resources in the hospital and we attempt to find a fair/balanced schedule for them, taking into account a set of constraints and the nurses' stated preferences, concerning the different shifts. We propose a particle swarm optimization algorithm to solve the ANSP. Finally, we compare our technique with previous results obtained using integer programming.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Anesthesiology Nurse Scheduling using Particle Swarm Optimization

Altamirano¹,

Riff²,

Araya³

et al. 2012

IJCIS

View full text Add to dashboard Cite

show abstract

“…Operations research methodology is well suited to addressing the mid-term planning problem, and a lot of research has been carried out within this field during the last five decades. Some examples can be found in Dowsland [9], Dowsland and Thompson [10], Bard and Purnomo [3], Berrada et al [5], and Jaumard et al [17].…”

Section: Mid-term Planningmentioning

confidence: 99%

Automatic Scheduling of Nurses: What Does It Take in Practice?

Rönnberg

Larsson

Bertilsson

2012

Springer Optimization and Its Applications

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Abstract:Many hospital wards need to be staffed by nurses around the clock every day of the year, and because of that, many nurses have to work irregular hours and according to schedules that have a great impact on their personal lives. Today most schedules are made by hand or with limited computer support, and this is both difficult and very time consuming. A common consequence of making the schedules by hand is that the outcome is neither favourable for the nurses nor satisfactory for the running of the ward. The nurse scheduling problem is well suited for addressing with operations research methods, but a challenge for automated scheduling of nurses is the ability to adapt the scheduling to the specific conditions on each ward. The intention of this paper is to provide a piece of practical experience that can help bridge the gap between advanced method development and the use of automatic nurse scheduling in practice. Our approach is to take on the real life scheduling problem with all its details, and to use a straightforward meta-heuristic in order to deliver automatically generated schedules. The contribution of the paper is based on the result of two case studies, which will provide insights into examples of real world nurse scheduling, including evaluation and feedback from the wards.

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“…When a problem is studied, it should be clearly understood [40]. At this stage, the main tasks is to review existing PSO methods for effective scheduling which are able to satisfy preferences of the doctors, supporting a fair and non-biased schedule in the hospital [41].…”

Section: Step 1: Identification Of the Problemmentioning

confidence: 99%

Untitled

2012

JCSCM

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Abstract:In this paper, a framework is presented to outline the steps of automating doctor scheduling process using an evolutionary algorithm. Particle Swarm Optimization (PSO) is applied within the framework to generate optimum work schedule for on-call doctors in emergency department at a public hospital in Malaysia. Currently, the doctor schedules are prepared by a head staff called captain who performs this tedious task manually where a lot of time is spent to perform this task. The main purpose of this research is to generate an optimum doctor schedule where the number of staff on duty is sufficed, individual preferences is fulfilled and all doctors are treated fairly. A systematic framework is established which can be applied to any automated on-call doctor scheduling system of emergency department in a Malaysian hospital.

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A multi-objective approach to nurse scheduling with both hard and soft constraints

Cited by 140 publications

References 13 publications

Anesthesiology Nurse Scheduling using Particle Swarm Optimization

Anesthesiology Nurse Scheduling using Particle Swarm Optimization

Automatic Scheduling of Nurses: What Does It Take in Practice?

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