Simple, Yet Fast and Effective Two-Phase Method for Nurse Rostering

Guessoum, F.; Haddadi, Salim; Gattal, E.

doi:10.1080/01966324.2019.1570882

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…In [38] a two-phase method to address the NSPLib dataset was presented. In the first phase, a generic Variable Fixing Heuristic (VFH) was utilized to reduce the size of the original problem.…”

Section: E Matheuristicsmentioning

confidence: 99%

“…The state-of-the-art methods for this dataset are Multi-Assignment Procedures Algorithm (MAPA) [31] and a hybrid of Variable Fixing Heuristic, Iterated Local Search and Mixed Integer Programming [39]. Other approaches that utilized this dataset are Harmony Search Algorithm [25] and a hybrid of Variable Fixing Heuristic and Mixed Integer Programming [38].…”

Section: Nurse Scheduling Problem Library Dataset (Nsplib)mentioning

confidence: 99%

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A Survey of the Nurse Rostering Solution Methodologies: The State-of-the-Art and Emerging Trends

et al. 2022

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This paper presents an overview of recent advances for the Nurse Rostering Problem (NRP) based on methodological papers published between 2012 to 2021. It provides a comprehensive review of the latest solution methodologies, particularly computational intelligence (CI) approaches, utilized in benchmark and real-world nurse rostering. The methodologies are systematically categorised (Heuristics, Meta-heuristics, Hyper-heuristics, Mathematical Optimisation, Matheuristics, Hybrid Approaches). The NRP benchmark repositories and the respective state-of-the-art methods are also presented. A distinctive feature of this survey is its focus on the emerging trends in terms of solution methodologies and benchmark datasets. Matheuristics, one of most popular methodologies in addressing the NRP, has been an emerging trend in recent years (2018 onwards). The INRC-I dataset is the most popular benchmark currently in use by researchers to test their algorithms. An in-depth discussion on the challenges and research opportunities is provided. We hope that the summary and analysis of the recently published NRP methodological papers in this survey is valuable for the CI and Operational Research (OR) communities especially early career researchers seeking to find gaps and identify emerging trends in this fast-developing, important research area.

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Section: E Matheuristicsmentioning

confidence: 99%

Section: Nurse Scheduling Problem Library Dataset (Nsplib)mentioning

confidence: 99%

A Survey of the Nurse Rostering Solution Methodologies: The State-of-the-Art and Emerging Trends

et al. 2022

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“…The coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) in March 2020. Since then, it has greatly impacted healthcare front liners in terms of psychological well-being, their morale and work performance due to long working hours under stressful conditions [20]. The urgency of having of an optimal roster is further underlined by the pandemic.…”

Section: Introductionmentioning

confidence: 99%

A 2-Stage Approach for the Nurse Rostering Problem

et al. 2022

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In this paper, we are addressing the NP-hard nurse rostering problem utilizing a 2-stage approach. In stage one, Monte Carlo Tree Search (MCTS) and Hill Climbing (HC) are hybridized in finding a feasible solution (satisfying all the hard constraints). We propose a new constant C value (which balances search diversification and intensification of MCTS) and tree policy/node selection function in the selection procedure of MCTS. In stage two, the feasible solution is further improved using Iterated Local Search (ILS) with Variable Neighbourhood Descent as the local search component. We introduce several unique neighbourhood structures for the ILS. In addition, we propose a novel perturbation strategy to allow the search to escape from local optimum. The proposed methodology is tested on the Shift Scheduling dataset (24 benchmark instances). New best results are reported for seven and two instances for the 10 and 60 minutes run respectively. An in-depth discussion on the attributes of the proposed methodology that lead to its good performance is provided.INDEX TERMS nurse rostering, hill climbing, monte carlo tree search, iterated local search, variable neighbourhood descent.

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“…Å finne effektive løsningsmetoder er derimot blitt viet mye oppmerksomhet. Noen populaere metoder er heltallsprogrammering (Bard & Purnomo, 2005a;Burke, Li & Qu, 2010), blandet heltallsprogrammering (Fügener, Brunner & Podtschaske, 2015;Yilmaz, 2012), kolonnegenerering (Bard & Purnomo, 2005b), heuristiske metoder (Bellanti et al, 2004;Guessoum, Haddadi & Gattal, 2020;Puente et al, 2009) og bruk av kunstig intelligens (Kumar et al, 2019). I denne delen av litteraturen er imidlertid fokus på stilistiske og generelle turnusplanleggingsproblemer (Burke et al, 2004).…”

Section: Introduksjonunclassified