A hyper-heuristic approach based upon a hidden Markov model for the multi-stage nurse rostering problem

Kheiri, Ahmed; Gretsista, Angeliki; Keedwell, Edward; Lulli, Guglielmo; Epitropakis, Michael G.; Burke, Edmund K.

doi:10.1016/j.cor.2021.105221

Cited by 23 publications

(11 citation statements)

References 39 publications

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“…Among the finalists of the competition were network flow based mixed integer linear programming [36], rotation based branch-and-price [28] and a sequence-based selection hyperheuristic [26]. Among the competitive approaches post competition were variable neighbourhood search [19], simulated annealing [10] and a hyper-heuristic based upon a hidden markov model [25].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

A 2-Stage Approach for the Nurse Rostering Problem

et al. 2022

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“…e results show that the DE algorithm has good performance. Kheiri et al [31] studied the multistage nurse rostering formulation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results show that the DE algorithm has good performance. Kheiri et al [ 31 ] studied the multistage nurse rostering formulation. They proposed a sequence-based selection hyper-heuristic using a statistical Markov model and an algorithm for building feasible initial solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Stage Method of Dimensioning and Scheduling Service Providers under Patient Demand Uncertainty

Shirneshan

Sadegheih

Hosseini-Nasab

et al. 2022

Computational Intelligence and Neuroscience

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Many researchers have studied the problem of dimensioning service providers and making shift schedules and have proposed various methods to solve it. Considering the importance and complexity of health care, this research is conducted through the integrated dimensioning and scheduling of service providers under patient demand uncertainty. In the first stage, a robust approach is adopted to determine the minimum number of required service providers. In the second stage, a monthly schedule is devised for service providers, and a two-stage stochastic program is used to solve the problem. To this end, an improved sample average approximation method considers different contracts and skills to determine a near-optimal schedule by minimizing the service providers’ regular working hours, overtime, and penalties for idle hours. In the first stage, considering the highest level of conservatism, equal to 7.6, a 19.38% cost increase is created compared to the nominal problem. In the second stage, by applying different clustering methods in the SAA algorithm and comparing them, the k-means++ algorithm obtains a good upper and lower bound and achieves a near-optimal solution in the shortest time. This research deals with the Iranian Health Control Center as a case study. The proposed method can yield the appropriate number of service providers based on monthly workloads and make the least undesirable schedules for service providers. Hence, managers can overcome patient issues’ uncertainty by assigning various service providers to each scheduling period.

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“…The first runner up (Legrain et al [26]) implemented a robust online stochastic algorithm that embeds a primaldual algorithm within a sample-average approximation (SAA). The third place winner (Kheiri et al [27]) proposed a sequence-based selection hyper-heuristic approach based upon a hidden Markov model. For more details on NRP and the recent solution methodologies, refer to [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

An Improved Discrete Grey Wolf Optimizer for the Nurse Rostering Problem

Ngoo

Goh

Kendall

2022

Preprint

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In this paper, an improved discrete Grey Wolf Optimizer (ID-GWO) is proposed to address a benchmark nurse rostering problem (NRP). We propose a discrete grey wolf optimizer (D-GWO) by utilizing the neighborhood structures commonly found in the literature as mutation operators. The D-GWO is further improved by modifying the crossover operator to enhance the algorithm’s exploitation capability. We propose two new mutation operators to improve the exploration capability of the algorithm and we apply probabilistic selection on the mutation operators to upgrade the performance of the algorithm. The proposed methodology is tested on the second International Nurse Rostering Competition (INRC-II) dataset. It performs better than 6 other entries in the competition. An in-depth discussion on the behavior and performance of the algorithm is provided.

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A hyper-heuristic approach based upon a hidden Markov model for the multi-stage nurse rostering problem

Cited by 23 publications

References 39 publications

A 2-Stage Approach for the Nurse Rostering Problem

A 2-Stage Approach for the Nurse Rostering Problem

A Two-Stage Method of Dimensioning and Scheduling Service Providers under Patient Demand Uncertainty

An Improved Discrete Grey Wolf Optimizer for the Nurse Rostering Problem

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