A metaheuristics approach to the nurse rostering problem

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

Users of hospital personnel planning software cope with the complex task of translating their needs into several constraints of a very different nature and with differing cost parameters. We present a multi criteria evolutionary approach, which overcomes some of the practical difficulties that personnel schedulers in hospitals often face.

2011

The Nurse Rostering Problem (NRP) is one of the NP-hard problems that are difficult to solve for optimality. NRP deals with the distribution of working shifts to the staff nurses at healthcare organizations under given rules. Normally, NRP aims at generating a duty roster that satisfies all of the hard constraints (mandatory) and as many soft constraints (optional) as possible. This work introduces a greedy constructive heuristic algorithm, based on building two patterns of two-week's duration that satisfies all of the hard constraints and several soft constraints. The first pattern is designed mainly to fulfill the night shift coverage whilst the second pattern is concerned with morning and evening shifts only. If the solution is not feasible (for example the coverage is not met), a repairing mechanism algorithm is applied until a feasible solution is reached. Simulated Annealing (SA) is then used to improve the constructed solution. A real dataset from Universiti Kebangsaan Malaysia Medical Center (UKMMC) is used in this work to test the proposed approach. Currently, the duty roster at UKMMC is still constructed manually. Promising results have been obtained and presented in this paper.

2010

Nurse rostering is an important 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 optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.

The problem of rostering nurses to shift duties and off days with several hard organizational and soft preferences constraints was studied. Many previous studies have been carried out to solve similar problems but there is no one solution technique that can be considered as the best. Thus, the study explores the use of Tabu Search (TS) technique to solve a particular nurse rostering problem (NRP). A TS algorithm with special representation and innovative neighborhood generations was constructed. Two variations of the neighbor were introduced. The solution is in the form of work schedule or roster which is able to assign effective and efficient shift duties.

International Journal of Service Science, Management, Engineering, and Technology, 2022

The main challenge of Nurse Scheduling Problem (NSP)is designing a nurse schedule that satisfies nurses preferences at minimal cost of violating the soft constraints. This makes the NSP an NP-hard problem with no perfect solution yet. In this study, two meta-heuristics procedures: Genetic Algorithm (GA) and Tabu Search (TS) memory was applied for the development of an automatic hospital nurse scheduling system (GATS_NSS). The data collected from the nursing services unit of a Federal Medical Centre (FMC) in Nigeria with 151 nursing staffs was preprocessed and adopted for training the GATS_NSS. The system was implemented in Java for Selection, Evaluation and Genetic Operators (Crossover and Mutation) of GA alongside the memory properties of TS. Nurses’ shift and ward allocation was optimized based on defined constraints of the case study hospital and the results obtained showed that GAT_NSS returned an average accuracy of 94%, 99% allocation rate, 0% duplication, 0.5% clash and an av...

Applied Soft Computing, 2020

Please cite this article as: P.-S. Chen and Z.-Y. Zeng, Developing two heuristic algorithms with metaheuristic algorithms to improve solutions of optimization problems with soft and hard constraints: An application to nurse rostering problems,

Computing Research Repository, 2008

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using cooperating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

IEEE Congress on Evolutionary Computation, 2002

Abstract: Users of hospital personnel planning software cope with the complex task of translating their needs into several constraints of a very different nature and with differing cost parameters. We present a multi criteria evolutionary approach, which overcomes some of the practical difficulties that personnel schedulers in hospitals often face.

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL), 2014

Hyper-heuristic (HH) is a higher level heuristic to choose from a set of heuristics applicable for the problem on hand. In this paper, a Harmony Search-based Hyper-heuristic (HSHH) approach is tested in solving nurse rostering problems (NRP). NRP is a complex scheduling problem of assigning given shifts to a given nurses. We test the proposed method by using the First International Nurse Rostering Competition 2010 (INRC2010) dataset. Experimentally, the HSHH approach achieved comparable results with the comparative methods in the literature.

IOP Conference Series: Materials Science and Engineering, 2019

Nurse scheduling problem is an essential problem in hospital, especially in emergency department. Emergency department is very busy unit in hospital because it receives patients in emergency situation, therefore nurses in this department are obliged to be ready for 24 hours. Working hours allocation of nurses in this department is not equally, so nurse’s preferences cannot be fulfilled yet. In this study, genetic algorithms are used to minimize unfulfilled preferences of nurse that called violations. Genetic algorithm represents solution candidate using random chromosomes. The selection used is elitist, the crossover used is one point cut crossover, and mutation used is reciprocal exchange mutation. Based on experiment, the optimal parameters are obtained, size of population is 80, mutation rate is 0.01, and iteration is 10000. Result is schedule of nurses for 30 days in emergency department that generated 74 violations.