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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Automated personal course scheduling adaptive spreading activation model
TL;DR: This paper developed a system that generates course schedules automatically, using spreading activation on a course network, where the problem is formalized as a constraint satisfaction/ optimization problem.
Book ChapterDOI
Simulation Analysis of the Break Assignment Problem Considering Area Coverage in Emergency Fleets.
Dora Novak,Marin Lujak +1 more
TL;DR: In this paper, the authors proposed a simplification of the break assignment problem considering area coverage (BAPCAC) proposed by Lujak et al. in [1].
A Flexible Distributed Scheduling Scheme for Dynamic ESG Environments
TL;DR: In this article, a holonic multi-objective evolutionary algorithm (MOEA) is proposed to produce robust and flexible distributed schedules within a dynamic ESG mission environment, such as asset break down, appearance of new events, node failures, etc.
Book ChapterDOI
On the Constraint Satisfaction Method for University Personal Course Scheduling
TL;DR: It is not easy for students to generate manually a course schedule from a large number of combination of classes, due to various constraints and/or criteria, especially for the freshman in the university.
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.