A Hybrid Tabu Search Algorithm for the Nurse Rostering Problem

Burke, Edmund K.; Causmaecker, Patrick De; Berghe, Greet Vanden

doi:10.1007/3-540-48873-1_25

Cited by 106 publications

(124 citation statements)

References 6 publications

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“…In order for the choice function based hyperheuristics to initialise the values of f 1 , f 2 , f 3 , and F for each neighbourhood, we randomly call the neighbourhoods for an initial warm-up period. This warm-up period is included in the time allowed to the choice function based hyperheuristics.…”

Section: Resultsmentioning

confidence: 99%

“…The small term ε/10η should enable every neighbourhood, no matter how bad, to be able to come around and diversify the solution after every other neighbourhood has been visited about 10 times. The fourth DECOMPCHOICE method considers the individual components f 1 , f 2 and f 3 , of F. It tries the (up to four) low level heuristics which yield the best values of f 1 , f 2 , f 3 , and F and performs the best move yielded by one of these low level heuristics..…”

Section: The Choice Functionmentioning

confidence: 99%

“…al. [2,3] where they employ a hybrid tabu search algorithm to schedule nurses in a Belgian hospital. Dodin et.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hyperheuristic Approach to Scheduling a Sales Summit

Cowling

Kendall

Soubeiga

2001

Lecture Notes in Computer Science

409

447

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Abstract. The concept of a hyperheuristic is introduced as an approach that operates at a higher lever of abstraction than current metaheuristic approaches. The hyperheuristic manages the choice of which lowerlevel heuristic method should be applied at any given time, depending upon the characteristics of the region of the solution space currently under exploration. We analyse the behaviour of several different hyperheuristic approaches for a real-world personnel scheduling problem. Results obtained show the effectiveness of our approach for this problem and suggest wider applicability of hyperheuristic approaches to other problems of scheduling and combinatorial optimisation.

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Section: Resultsmentioning

confidence: 99%

Section: The Choice Functionmentioning

confidence: 99%

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A Hyperheuristic Approach to Scheduling a Sales Summit

Cowling

Kendall

Soubeiga

2001

Lecture Notes in Computer Science

409

447

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“…Some earliest techniques utilized for the NRP include integer programming [4], [5], goal programming [6], case-based reasoning [7], [8] and constraint Programming [9], [10]. In the recent time, some of the metaheuristic techniques that have also been employed for the NRP are local search-based approaches, which include tabu search [11], [12], simulated annealing [13], variable neighbourhood structures (VNS) [14], [15]. Others are population-based approaches like ant colony optimization [16], genetic algorithm (GA) [17], [18], harmony search algorithm (HSA) [19], [20], particle swarm optimization [21].…”

Section: Introductionmentioning

confidence: 99%

Solving Nurse Rostering Problem Using Artificial Bee Colony Algorithm

Awadallah

Al-Betar

Khader

2015

The 7th International Conference on Information Technology

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Abstract-Artificial bee colony algorithm(ABC) is proposed as a new nature-inspired algorithm which has been successfully utilized to tackle numerous class of optimization problems belongs to the category of swarm intelligence optimization algorithms. The major focus of this paper is to show that ABC could be used to generate good solutions when adapted to tackle the nurse rostering problem (NRP). In the proposed ABC for the NRP, the solution methods is divided into two phases. The first uses a heuristic ordering strategy to generate feasible solutions while the second phase employs the usage of ABC algorithm in which its operators are utilized to enhance the feasible solutions to their optimality. The proposed algorithm is tested on a set of 69 problem instances of the dataset introduced by the First International Nurse Rostering Competition 2010 (INRC2010). The results produced by the proposed algorithm are very promising when compared with some existing techniques that worked on the same dataset. Further investigation is still necessary for further improvement of the proposed algorithm.Keywords-Nurse Rostering; Artificial Bee Colony Algorithm; Swarm Intelligence Method; Nature-inspired algorithm I. INTRODUCTION The nurse rostering problem (NRP) is among the timetabling problem that is widely investigated by the researchers in the domain of operations research and artificial intelligence. The NRP as a NP-hard problem is described as an assignment of a set of qualified nurses to a different set of shifts over a predetermined scheduling period, subject to a set of hard and soft constraints. The hard constraints is the type that must be fulfilled for the roster to be feasible whereas the violations of soft constraints in the NRP are allowed but should be minimized as much as possible. It is noteworthy that the quality of the roster is determined by the satisfaction of the soft constraints in a feasible roster. The basic objective of NRP is to generate a feasible roster of high quality. However, studies in NRP domain have shown that it is almost impossible to find a roster that satisfies all constraints, since the NRP is classified as a combinatorial optimization problem This paper tackles the NRP dataset proposed by the First International Nurse Rostering Competition (INRC2010), which is organized by the CODeS research group at Katholieke Universiteit Leuven in Belgium, SINTEF Group in Norway and the University of Udine in Italy. The dataset of the INRC2010 is classified into three tracks: sprint, medium, and long datasets, which are varied in size and complexity. Each track is grouped into four categories in accordance with their publication time at the competition: early, late, hidden, and hint. Few techniques proposed to solve the INRC2010 dataset during and after the competition are review as follows. Valouxis et al. in [26] applied Integer Programming (IP) to tackle the NRP using INRC2010 dataset in which their solution method consists of two stages: the first stage consists of assigning different nurses to ...

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“…Variations in research are observed in the areas of application and in the definition of the problem, as well as in the employed solution methods: Jaumard (Jaumard et al, 1998), Chun (Chun et al, 2000), Barboza (Barboza et al, 2003), Meisels and Schaerf (2003), and Özcan (2005). Currently, in the literature one can find real cases of the rostering problem treated with metaheuristics: Simulated Annealing as in Dowling (Dowling et al, 1997), Tabu Search (TS) as in Dowsland (Dowsland, 1998) and Burke (Burke et al, 1998), Genetic Algorithms (GA) as in Burke (Burke et al, 2001), Özcan (Özcan, 2005), Yeh (2007) and Tsai (Tsai and Li, 2009), and Iterated Local Search. Due to the large number of variables and constraints involved, it is classified as a NP-Hard optimization problem (Burke et al, 2003;Osogami and Imai, 2000).…”

Section: Introductionmentioning

confidence: 99%

A metaheuristics approach to the nurse rostering problem

Gómez

Santos

Poltosi

2012

Journal of Applied Computing Research

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Abstract. Health care providers are affected by problems of personnel costs. Usually, the generation of rosters is a hand-made and time-consuming task and does not always comply with the legislation and the internal rules. The article presents an approach to roster generation for nursing technicians according to legal and internal restrictions and in a satisfactory period of time. It is also designed to give employees a higher level of satisfaction concerning their day off preferences and a fair distribution of unpopular shifts. The article's proposal is to develop a hybrid system formed by a Tabu Search metaheuristic combined with a genetic algorithm. Experiments were carried out with artificial test cases based on real data. The results obtained were satisfactory, showing the feasibility of the solution in all tests performed.

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A Hybrid Tabu Search Algorithm for the Nurse Rostering Problem

Cited by 106 publications

References 6 publications

A Hyperheuristic Approach to Scheduling a Sales Summit

A Hyperheuristic Approach to Scheduling a Sales Summit

Solving Nurse Rostering Problem Using Artificial Bee Colony Algorithm

A metaheuristics approach to the nurse rostering problem

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