Deep Q-Network-Based Neighborhood Tabu Search for Nurse Rostering Problem

20 Pages Posted: 17 Nov 2023

See all articles by Xinzhi Zhang

Xinzhi Zhang

Shenzhen University

Qingling Zhu

Shenzhen University

Qiuzhen Lin

Shenzhen University

Wei-Neng Chen

South China University of Technology

Jianqiang Li

Shenzhen University

Carlos Artemio Coello Coello

Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN)

Abstract

The Nurse Rostering Problem (NRP) is important for hospital management, which aims to balance both the needs of employees and the requirements of hospital operations. Currently, most studies often use local search methods with adaptive neighborhood search (ANS) or variable neighborhood descent (VND) to efficiently tackle the NRP. However, the use of ANS or VND in their local search strategies typically focuses on optimizing the current solution without considering the potential long-term consequences of these local improvements, which may easily fall into local optima and resultantly limit the final performance. Thus, this paper proposes a Deep Q-network-based Neighborhood Tabu Search (called DQN-NTS) method for tackling the NRP. Our method is a first attempt to train a deep Q-network (DQN) as a selection method for identifying effective sequences of neighborhoods, which can learn good transitions among the neighborhoods. We model the neighborhood search as a Markov model, where the current solution represents the state, and the reward signifies the improvement in the objective function. This approach enables DQN to consider neighborhood transition sequences that have historically obtained superior solutions, thereby helping to avoid local optima and achieve global optima by considering long-term impacts. Moreover, tabu search is combined with the DQN to iterate better solutions and diversify the search space by changing the weights of nurses when stagnating at the local optimum. Experimental results indicate that DQN-NTS outperforms other state-of-the-art tabu search methods with ANS and VND when running on the Second International Nurse Rostering Competition dataset.

Keywords: Nurse Rostering Problem, Neighborhood Selection, Deep Q-network, Tabu Search

Suggested Citation

Zhang, Xinzhi and Zhu, Qingling and Lin, Qiuzhen and Chen, Wei-Neng and Li, Jianqiang and Coello Coello, Carlos Artemio, Deep Q-Network-Based Neighborhood Tabu Search for Nurse Rostering Problem. Available at SSRN: https://ssrn.com/abstract=4635872 or http://dx.doi.org/10.2139/ssrn.4635872

Xinzhi Zhang

Shenzhen University ( email )

3688 Nanhai Road, Nanshan District
Shenzhen, 518060
China

Qingling Zhu

Shenzhen University ( email )

3688 Nanhai Road, Nanshan District
Shenzhen, 518060
China

Qiuzhen Lin (Contact Author)

Shenzhen University ( email )

3688 Nanhai Road, Nanshan District
Shenzhen, 518060
China

Wei-Neng Chen

South China University of Technology ( email )

Wushan
Guangzhou, AR 510640
China

Jianqiang Li

Shenzhen University ( email )

3688 Nanhai Road, Nanshan District
Shenzhen, 518060
China

Carlos Artemio Coello Coello

Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN) ( email )

07360 Mexico, D.F.
Mexico

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