A hybrid metaheuristic case-based reasoning system for nurse rostering

Beddoe, Gareth; Petrović, Sanja; Li, Jingpeng

doi:10.1007/s10951-008-0082-8

Cited by 67 publications

(47 citation statements)

References 28 publications

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“…We do indeed not expect all combinations to occur in practice and it would in fact be interesting to position the set of studied problems within the space of all the possibilities. More interestingly, the table highlights similarity of problems in independent studies, for example (Osogami and Imai 2000) and (Maenhout and Vanhoucke 2008); (Beddoe et al 2009;Petrovic 2006, 2007) and (Chun et al 2000).…”

Section: Mmentioning

confidence: 75%

A categorisation of nurse rostering problems

Causmaecker

Berghe²

2010

J Sched

103

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Personnel rostering has received ample attention in recent years. Due to its social and economic relevance and due to its intrinsic complexity, it has become a major subject for scheduling and timetabling researchers. Among the personnel rostering problems, nurse rostering turned out to be particularly complex and difficult. In this paper, we propose a notation for nurse rostering problems along the lines of the α|β|γ notation for scheduling. The system allows extension as well as refinement. It is the aim of the notation to facilitate problem description, classification and systematic study. It enables authors to position the problems in the vaster body of research on the subject. By developing this notation for nurse rostering, we hope that an extension of it will be applicable to a broader domain of personnel rostering.

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Section: Mmentioning

confidence: 75%

A categorisation of nurse rostering problems

Causmaecker

Berghe²

2010

J Sched

103

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“…CBR scheduling systems in the second category reuse the operators for the resolution of the new problem [13]. A case describes a context in which a useful scheduling problem is used for repairing/adapting a scheduling plan to improve its quality, in terms of constraints' satisfaction [17]. Burke et al [13] have proposed a case-based hyper-heuristic to solve timetabling problems.…”

Section: Case-based Reasoningmentioning

confidence: 99%

“…Burke et al [13] have proposed a case-based hyper-heuristic to solve timetabling problems. Beddoe et al [17] have developed a CBR system to solve nurse scheduling problems. Finally, in CBR scheduling systems of third category, it is used the whole or part of previous solutions of problems to construct the solution of the new case.…”

Section: Case-based Reasoningmentioning

confidence: 99%

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

Madureira

Pereira

2010

2010 10th International Conference on Hybrid Intelligent Systems

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Abstract:A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

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“…Naturally, investigations of numerous techniques for tackling NRP in the timetabling domain have increased over the past five decades. 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].…”

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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A hybrid metaheuristic case-based reasoning system for nurse rostering

Cited by 67 publications

References 28 publications

A categorisation of nurse rostering problems

A categorisation of nurse rostering problems

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

Solving Nurse Rostering Problem Using Artificial Bee Colony Algorithm

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