IEEE Access (Jan 2022)

A 2-Stage Approach for the Nurse Rostering Problem

  • Say Leng Goh,
  • San Nah Sze,
  • Nasser R. Sabar,
  • Salwani Abdullah,
  • Graham Kendall

DOI
https://doi.org/10.1109/ACCESS.2022.3186097
Journal volume & issue
Vol. 10
pp. 69591 – 69604

Abstract

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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.

Keywords