Dynamic Programming with Approximation Function for Nurse Scheduling

Shi, Peng; Landa-Silva, Dario

doi:10.1007/978-3-319-51469-7_23

Cited by 7 publications

(7 citation statements)

References 12 publications

(17 reference statements)

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“…In these instances, each stage is a week under the competition setting. This section outlines the problem and its modelling as a Markov Decision Process proposed in a previous paper [6].…”

Section: The Multi-stage Nurse Rostering Problemmentioning

confidence: 99%

“…First, the pre-process phase sets up the search space. Then, the local phase is an enhancement of our previous work [6] for solving the weekly optimization problems. Finally, the global phase applies a lookahead policy for future demand evaluation.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Our previous work investigated ADP to solve NRP, where a value function approximation based method was proposed to tackle various instances of the NRP [6]. However, the computation time required for constructing solution samples and the memory space required for recording rewards increased exponentially for larger problem instances.…”

Section: Introductionmentioning

confidence: 99%

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Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Shi

Landa-Silva

2017

Lecture Notes in Computer Science

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Abstract. An approximate dynamic programming that incorporates a combined policy, value function approximation and lookahead policy, is proposed. The algorithm is validated by applying it to solve a set of instances of the nurse rostering problem tackled as a multi-stage problem. In each stage of the problem, a weekly roster is constructed taking into consideration historical information about the nurse rosters in the previous week and assuming the future demand for the following weeks as unknown. The proposed method consists of three phases. First, a pre-process phase generates a set of valid shift patterns. Next, a local phase solves the weekly optimization problem using value function approximation policy. Finally, the global phase uses lookahead policy to evaluate the weekly rosters within a lookahead period. Experiments are conducted using instances from the Second International Nurse Rostering Competition and results indicate that the method is able to solve large instances of the problem which was not possible with a previous version of approximate dynamic programming.

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“…In these instances, each stage is a week under the competition setting. This section outlines the problem and its modelling as a Markov Decision Process proposed in a previous paper [6].…”

Section: The Multi-stage Nurse Rostering Problemmentioning

confidence: 99%

Section: Proposed Algorithmmentioning

confidence: 99%

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Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Shi

Landa-Silva

2017

Lecture Notes in Computer Science

Self Cite

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“…Algorithms are powerful tools to search for high-quality solutions. e exact algorithms employed for nurse rostering problem include integer programming algorithm [12], dynamic programming algorithm [13], branch and bound method [14], and column generation method [15]. However, when the problem becomes complex and highly constrained, it may consume numerous computational time to search for an optimal solution with an exact algorithm.…”

Section: Introductionmentioning

confidence: 99%

Personnel Scheduling Problem under Hierarchical Management Based on Intelligent Algorithm

Huang

Gao

et al. 2021

Complexity

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This paper studies a special scheduling problem under hierarchical management in nurse staff. This is a more complex rostering problem than traditional nurse scheduling. The first is that the rostering requirements of charge nurses and general nurses are different under hierarchical management. The second is that nurses are preferable for relative fair rather than absolute fair under hierarchical management. The model aims at allocating the required workload to meet the operational requirements, weekend rostering preferences, and relative fairness preferences. Two hybrid heuristic algorithms based on multiobjective grey wolf optimizer (MOGWO) and three corresponding single heuristic algorithms are employed to solve this problem. The experimental results based on real cases from the Third People’s Hospital, Panzhihua, China, show that MOGWO does not as good as it does on other engineering optimization. However, the hybrid algorithms based on MOGWO are better than corresponding single algorithms on generational distance (GD) and spacing (SP) of Pareto solutions. Furthermore, for relative fair rostering objective, NSGAII-MOGWO has more power to find the optimal solution in the dimension of relative fairness.

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“…Nurse rostering is a difficult combinatorial optimisation problem for which many solution techniques have been proposed in the literature [2,3]. In our previous research, the suitability of ADP to solve the Nurse Rostering Problem (NRP) was investigated by approaching NRP as a Markov Decision Process [4]. The approximation function focused on selecting actions that satisfy the principle of optimality [5] but not all were covered.…”

Section: Introductionmentioning

confidence: 99%

Lookahead Policy and Genetic Algorithm for Solving Nurse Rostering Problems

Shi

Landa-Silva

2019

Machine Learning, Optimization, and Data Science

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Previous research has shown that value function approximation in dynamic programming does not perform too well when tackling difficult combinatorial optimisation problem such as multi-stage nurse rostering. This is because the large action space that need to be explored. This paper proposes to replace the value function approximation by a genetic algorithm in order to generate solutions to the stages before applying the lookahead policy to evaluate the future effect of decisions made in previous stages. Then, the paper proposes a hybrid approach that generates sets of weekly rosters through a genetic algorithm for consideration by the looahead procedure that assembles a solution for the whole planning horizon of several weeks. Results indicate that this hybrid between an evolutionary algorithm and the lookahead policy mechanism from dynamic programming performs more competitive than the value function approximation dynamic programming investigated before. Results also show that the proposed algorithm is ranked well in respect of several other algorithms applied to the same set of problem instances. The intended contribution of this paper is towards a better understanding of how to successfully apply dynamic programming mechanisms to tackle difficult combinatorial optimisation problems.

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Dynamic Programming with Approximation Function for Nurse Scheduling

Cited by 7 publications

References 12 publications

Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Approximate Dynamic Programming with Combined Policy Functions for Solving Multi-stage Nurse Rostering Problem

Personnel Scheduling Problem under Hierarchical Management Based on Intelligent Algorithm

Lookahead Policy and Genetic Algorithm for Solving Nurse Rostering Problems

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