A hybrid variable neighbourhood search and dynamic programming approach for the nurse rostering problem

Abstract

Nurse Rostering is the activity of assigning nurses to daily shifts in order to satisfy the cover requirements, taking into account the operational requirements and nurses' preferences. The problem is usually modeled as sets of hard and soft constraints with an objective function to minimize violations of soft constraints. The nurse rostering problem is known to be NP-hard. Many metaheuristics were used to tackle this problem. One of the frequently used heuristics is the Variable Neighbourhood Search (VNS). The VNS is usually used as a standalone method or in integration with another exact or heuristic method. In this paper, a new hybrid VNS and Dynamic Programming based heuristic approach is proposed to handle the nurse rostering problem. In the proposed approach, two perturbation mechanisms are adopted simultaneously. The proposed approach is tested on two different benchmark data sets. A comparison with state-of-the-art methods from literature revealed the competitive performance of the proposed approach.2010 Mathematics Subject Classification. Primary: 90B35; Secondary: 90C59, 90C39.

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

“…Table IV shows the categorization of the solution methodologies for the nurse rostering problems. The most recent applications are Sequence-based Selection Hyperheuristic [50], a hybrid of Dynamic Programming and Variable Neighbourhood Search [1] and Population-based Local Search [2].…”

“…Abdelghany et al [1] proposed a new hybrid of Variable Neighbourhood Search (VNS) and Dynamic Programming (DP) to tackle the INRC-I dataset. An initial feasible solution was generated by using a greedy constructive heuristic.…”

“…Meta-heuristics seem to be a common approach for this dataset where the population based approach outnumbered the single solutionbased variant. The state-of-the-art methods for this dataset are a hybrid of Integer Programming and Hill Climbing [79], Adaptive Neighborhood Search (ANS) [52], Randomized Variable Neighborhood Search (RVNS) [87], a hybrid of Dynamic Programming and Variable Neighbourhood Search [1] and Population-based Local Search (PB-LS) [2].…”

A 2-Stage Approach for the Nurse Rostering Problem

“…Other finalists of the competition were; Burke and Curtois [7] applied an ejection method for small instances and a branch and price algorithm for medium and long instances; Nonobe [33] employed a tabu search, with a mechanism to dynamically control the tabu tenure and constraint weights; Bilgin et al [5] applied a hybrid approach where a greedy shuffle is deployed after running a hyper-heuristic for 80% of the computation time. Recent approaches for the nurse rostering problem include adaptive variable neghbourhood search [43], hybrid harmony search [3], randomized variable neighbourhood search [49], neutrality based iterated local search [32], population-based local search [2] and a hybrid of variable neighbourhood search and dynamic programming [1].…”

An Improved Discrete Grey Wolf Optimizer for the Nurse Rostering Problem

“…Other finalists in the competition were: Nonobe [17] who applied a tabu search to control the constraints weight and tabu tenure dynamically; Lu Zhipeng and Hao Jin-Kao [18] who implemented an adaptive neighborhood search that adaptively switches among three strategies (intermediate, diversification and intensive searches); Burke and Curtois [19] who applied a variable depth search (ejection chain based method) on sprint instances and a branch and price algorithm on medium and long instances. Recent approaches for the INRC-I are a hybrid of dynamic programming and variable neighborhood search [20], population-based local search [21], randomized variable neighborhood search [22] and hybrid harmony search [23].…”