A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Abstract

Abstract-Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.

Is Evolutionary Computation Evolving Fast Enough?

“…However, they have been used in industrial applications so we thought it was worth briefly mentioning them here. We also note that the leading EC journal (IEEE Transactions on Evolutionary Computation) has previously reported work that includes these methodologies [29], [30], albeit hybridized with an evolutionary algorithm. Simulated annealing and tabu search has been reported as being deployed in industry, including Oil Field Drilling [31], Sports [32], [33], [34], [35], Vehicle Routing [36], Underground Mine Layouts [37] and Personnel Scheduling [38].…”

Workforce planning incorporating skills: State of the art

“…Wu et al [117] and Wu et al [116] therefore compare the results obtained with their exact branch and bound technique to the results obtained with a heuristic algorithm. [1,2,3,7,9,10,11,15,16,17,21,22,24,25,27,29,32,33,34,36,38,39,41,48,49,52,55,57,64,66,68,72,73,74,80,82,83,87,90,98,100,103,104,107,109,110,113,114,116,…”

A harmony search algorithm for nurse rostering problems

“…In this work, we have implemented the stochastic ranking based GA for NRPs proposed in [49] using the same parameter settings as follows: the number of individuals = 1000, crossover rate = 0.75 and mutation rate = 0.02. For the selection, we used tournament selection with stochastic ranking (tournament size = 7) and elitism.…”

“…For the selection, we used tournament selection with stochastic ranking (tournament size = 7) and elitism. For the crossover operator, we used a single point crossover as in [49] and [50]. The mutation operator is carried out by randomly changing one shift pattern for one nurse selected randomly.…”