An indirect genetic algorithm for a nurse-scheduling problem
Uwe Aickelin,Kathryn A. Dowsland +1 more
TLDR
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.read more
Citations
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Journal ArticleDOI
An evolutionary algorithm based on constraint set partitioning for nurse rostering problems
TL;DR: This paper investigates a large-scale NRP in a real-world setting, i.e., Chinese NRP, which requires us to arrange many nurses across a 1-month scheduling period, and proposes a single-individual EA for the CNRP.
Proceedings ArticleDOI
Test and evaluation of a SoS using a prescriptive and adaptive testing framework
John Hess,Ricardo Valerdi +1 more
TL;DR: By facilitating rapid planning and replanning, the PATFrame reasoning engine will enable users to use information learned during the test process to improve the effectiveness of their own testing rather than simply follow a preset schedule.
Journal ArticleDOI
Progress control in iterated local search for nurse rostering
TL;DR: This paper describes an approach in which a local search technique is alternated with a process which ‘jumps’ to another point in the search space, and proposes a model for estimating the quality of this new local optimum.
Journal ArticleDOI
A Strategy to Improve Performance of Genetic Algorithm for Nurse Scheduling Problem
TL;DR: The experimental results showed that the suggested method generated a nurse scheduling faster in time and better in quality compared to the traditional genetic algorithm.
Journal ArticleDOI
Operations research applications in hospital operations: Part II
TL;DR: A timeline of events in US healthcare from the late 1940s to 2015 is developed and separate the timeline into four eras: Expansion, Cost Control, Reform, and Accountability.
References
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Book
Genetic algorithms in search, optimization, and machine learning
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.
Genetic algorithms in search, optimization and machine learning
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.
Book
Adaptation in natural and artificial systems
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.
Book
Handbook of Genetic Algorithms
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.