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Journal ArticleDOI

An indirect genetic algorithm for a nurse-scheduling problem

20 Apr 2004-Computers & Operations Research (Elsevier)-Vol. 31, Iss: 5, pp 761-778
TL;DR: This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital that is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.
About: This article is published in Computers & Operations Research.The article was published on 2004-04-20 and is currently open access. It has received 360 citations till now. The article focuses on the topics: Crossover & Nurse scheduling problem.
Citations
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Journal ArticleDOI
TL;DR: This review discusses nurse rostering within the global personnel scheduling problem in healthcare and critically evaluates solution approaches which span the interdisciplinary spectrum from operations research techniques to artificial intelligence methods.
Abstract: Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. The need for quality software solutions is acute for a number of reasons. In particular, it is very important to efficiently utilise time and effort, to evenly balance the workload among people and to attempt to satisfy personnel preferences. A high quality roster can lead to a more contented and thus more effective workforce. In this review, we discuss nurse rostering within the global personnel scheduling problem in healthcare. We begin by briefly discussing the review and overview papers that have appeared in the literature and by noting the role that nurse rostering plays within the wider context of longer term hospital personnel planning. The main body of the paper describes and critically evaluates solution approaches which span the interdisciplinary spectrum from operations research techniques to artificial intelligence methods. We conclude by drawing on the strengths and weaknesses of the literature to outline the key issues that need addressing in future nurse rostering research.

897 citations


Cites background or methods or result from "An indirect genetic algorithm for a..."

  • ...Both the problem and the model are comparable to those presented by Aickelin and Dowsland (2000) and Dowsland (1998) (discussed in Section 3.5). However, Blau and Sear take over- and understaffing into account whereas Aickelin and Dowsland consider solutions with coverage deficiencies as infeasible. The contributions in Aickelin and Dowsland (2000) and Dowsland (1998) are also concerned with achieving optimality. Blau (1985), tries to equalise the distribution of unpopular work in addition to the frequency with which employees are granted requests for shifts or days....

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  • ...Both the problem and the model are comparable to those presented by Aickelin and Dowsland (2000) and Dowsland (1998) (discussed in Section 3....

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  • ...Both the problem and the model are comparable to those presented by Aickelin and Dowsland (2000) and Dowsland (1998) (discussed in Section 3.5). However, Blau and Sear take over- and understaffing into account whereas Aickelin and Dowsland consider solutions with coverage deficiencies as infeasible. The contributions in Aickelin and Dowsland (2000) and Dowsland (1998) are also concerned with achieving optimality. Blau (1985), tries to equalise the distribution of unpopular work in addition to the frequency with which employees are granted requests for shifts or days. This is one of the earlier attempts (besides Warner’s (Warner, 1976)) to evenly treat personnel with respect to workload and preferences. In later contributions (see also Table 18 in Appendix B), the distribution of work among people is often arranged via additional constraints. Anzai and Miura (1987), present a cyclic descent algorithm for a ward in which the personnel members are identical (with respect to skills and work regulations). Anzai and Miura state that their model is too simplified for practical applications. Kostreva and Jennings (1991), solve the nurse scheduling problem in two phases. Groups of feasible schedules are calculated in a first step. The groups respect the minimum staffing requirements and each individual schedule fulfils all major working constraints. In the second phase, the best possible ‘aversion score’, which is based on the preferences of the individual nurses (Kostreva and Genevier, 1989), is calculated. The tackled problems are not complex. All the skill categories are scheduled independently, which comes down to a decomposition into partial problems. Modern nurse rostering practice is not usually compatible with this type of approach. Schaerf and Meisels (1999), present a general definition of employee timetabling problems....

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  • ...Both the problem and the model are comparable to those presented by Aickelin and Dowsland (2000) and Dowsland (1998) (discussed in Section 3.5). However, Blau and Sear take over- and understaffing into account whereas Aickelin and Dowsland consider solutions with coverage deficiencies as infeasible. The contributions in Aickelin and Dowsland (2000) and Dowsland (1998) are also concerned with achieving optimality. Blau (1985), tries to equalise the distribution of unpopular work in addition to the frequency with which employees are granted requests for shifts or days. This is one of the earlier attempts (besides Warner’s (Warner, 1976)) to evenly treat personnel with respect to workload and preferences. In later contributions (see also Table 18 in Appendix B), the distribution of work among people is often arranged via additional constraints. Anzai and Miura (1987), present a cyclic descent algorithm for a ward in which the personnel members are identical (with respect to skills and work regulations). Anzai and Miura state that their model is too simplified for practical applications. Kostreva and Jennings (1991), solve the nurse scheduling problem in two phases....

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  • ...Both the problem and the model are comparable to those presented by Aickelin and Dowsland (2000) and Dowsland (1998) (discussed in Section 3.5). However, Blau and Sear take over- and understaffing into account whereas Aickelin and Dowsland consider solutions with coverage deficiencies as infeasible. The contributions in Aickelin and Dowsland (2000) and Dowsland (1998) are also concerned with achieving optimality....

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Journal ArticleDOI
TL;DR: This paper presents a review of the literature on personnel scheduling problems and discusses the classification methods in former review papers, and evaluates the literature in the many fields that are related to either the problem setting or the technical features.

706 citations

Journal ArticleDOI
TL;DR: A brief overview, in the form of a bibliographic survey, of the many models and methodologies available to solve the nurse rostering problem is presented.

498 citations

Journal ArticleDOI
TL;DR: This paper surveys several applications of Operations Research in the domain of Healthcare and highlights current research activities, focusing on a variety of optimisation problems as well as solution techniques used for solving the Optimisation problems.

339 citations


Cites methods from "An indirect genetic algorithm for a..."

  • ...Other metaheuristic approaches can be found as follows: Evolutionary Algorithms in Aickelin et al. (2007), Aickelin and Dowsland (2004) and Aickelin and Dowsland (2000); Tabu Search in Bester et al. (2007), Ikegami and Niwa (2003), Dowsland and Thompson (2000) and Dowsland (1998); Scatter Search in…...

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Journal ArticleDOI
TL;DR: A review and classification of the literature regarding workforce planning problems incorporating skills to present a combination of technical and managerial knowledge to encourage the production of more realistic and useful solution techniques.
Abstract: This paper presents a review and classification of the literature regarding workforce planning problems incorporating skills. In many cases, technical research regarding workforce planning focuses very hard on the mathematical model and neglects the real life implications of the simplifications that were needed for the model to perform well. On the other hand, many managerial studies give an extensive description of the human implications of certain management decisions in particular cases, but fail to provide useful mathematical models to solve workforce planning problems. This review will guide the operations researcher in his search to find useful papers and information regarding workforce planning problems incorporating skills. We not only discuss the differences and similarities between different papers, but we also give an overview of the managerial insights. The objective is to present a combination of technical and managerial knowledge to encourage the production of more realistic and useful solution techniques.

207 citations


Cites background from "An indirect genetic algorithm for a..."

  • ...), only a few incorporate decisions about the composition of shifts in their model [1, 2, 3, 7, 10, 11, 12, 24, 27, 28, 33, 48, 72, 75, 103, 104, 108]....

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  • ...Quality [15, 93, 108, 109] Task restrictions [1, 2, 3, 4, 7, 9, 10, 11, 12, 16, 17, 19, 20, 21, 23, 24, 26, 28, 31, 32, 33, 35, 36, 38, 40, 42, 48, 49, 51, 52, 54, 55, 56, 57, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 80, 81, 83, 84, 85, 88, 89, 91, 92, 94, 97, 99, 100, 102, 103, 104, 105, 107, 111, 114, 115, 121]...

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  • ...89, 91, 94, 100, 102, 105, 111] Nurse grade [1, 2, 3, 9, 10, 11, 16, 17, 21, 24, 26, 31, 32, 38, 65, 68, 70, 88, 92, 104] User definable/undefined [12, 15, 19, 20, 22, 23, 33, 49, 55, 67, 68, 69, 72, 75, 77, 80, 81, 84, 85, 97, 99, 101, 115] Other [21, 54, 66, 93, 110, 121]...

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  • ...Services General [42, 51, 64, 66, 67, 75, 77, 87, 102, 105, 111] Health care [1, 2, 3, 9, 10, 11, 16, 17, 21, 24, 26, 27, 28, 31, 32, 38, 40, 55, 65, 68, 70, 88, 89, 92, 104] Maintenance [37, 56, 71]...

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  • ...Simulated annealing [9, 73, 104, 108, 117] Tabu search [21, 24, 31, 49, 73] Genetic algorithm [1, 2, 3, 9, 33, 57, 99, 111] Greedy algorithm [26, 38, 49] Other [7, 15, 16, 17, 23, 28, 32, 35, 37, 40, 52, 55, 56, 65, 67, 68, 69, 74, 75, 81, 84, 88, 91, 104, 110,...

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References
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Book
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,797 citations

01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

33,034 citations


"An indirect genetic algorithm for a..." refers background in this paper

  • ...The demand for nurses is fulfilled for every grade on every day and night: skRxaq ks iFj n i ijjkis , )( 1 ∀≥∑ ∑ ∈ = (2) Constraint set (1) ensures that every nurse works exactly one shift pattern from his/her feasible set, and constraint set (2) ensures that the demand for nurses is covered for…...

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Book
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)

32,573 citations


"An indirect genetic algorithm for a..." refers background in this paper

  • ...The rostering problem tackled in this paper can be described as follows....

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01 Jan 1989

12,457 citations


"An indirect genetic algorithm for a..." refers background in this paper

  • ...and mutation operators as explained for instance in Goldberg [ 20 ]....

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Book
01 Jan 1991
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Abstract: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. The remainder of the book is turned over to a series of short review articles by a collection of authors, each explaining how genetic algorithms have been applied to problems in their own specific area of interest. The first part of the book introduces the fundamental genetic algorithm (GA), explains how it has traditionally been designed and implemented and shows how the basic technique may be applied to a very simple numerical optimisation problem. The basic technique is then altered and refined in a number of ways, with the effects of each change being measured by comparison against the performance of the original. In this way, the reader is provided with an uncluttered introduction to the technique and learns to appreciate why certain variants of GA have become more popular than others in the scientific community. Davis stresses that the choice of a suitable representation for the problem in hand is a key step in applying the GA, as is the selection of suitable techniques for generating new solutions from old. He is refreshingly open in admitting that much of the business of adapting the GA to specific problems owes more to art than to science. It is nice to see the terminology associated with this subject explained, with the author stressing that much of the field is still an active area of research. Few assumptions are made about the reader's mathematical background. The second part of the book contains thirteen cameo descriptions of how genetic algorithmic techniques have been, or are being, applied to a diverse range of problems. Thus, one group of authors explains how the technique has been used for modelling arms races between neighbouring countries (a non- linear, dynamical system), while another group describes its use in deciding design trade-offs for military aircraft. My own favourite is a rather charming account of how the GA was applied to a series of scheduling problems. Having attempted something of this sort with Simulated Annealing, I found it refreshing to see the authors highlighting some of the problems that they had encountered, rather than sweeping them under the carpet as is so often done in the scientific literature. The editor points out that there are standard GA tools available for either play or serious development work. Two of these (GENESIS and OOGA) are described in a short, third part of the book. As is so often the case nowadays, it is possible to obtain a diskette containing both systems by sending your Visa card details (or $60) to an address in the USA.

6,758 citations