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
A Hybrid of Genetic Algorithm and Evidential Reasoning for Optimal Design of Project Scheduling: A Systematic Negotiation Framework for Multiple Decision-Makers
TL;DR: Traditional project scheduling methods inherently assume that the decision makers (DMs) are a unique entity whose acts are based on group rationality, but in practice, DMs’ reliance on individ...
Proceedings ArticleDOI
Application of quality function deployment and genetic algorithm for replacement of medical equipment
TL;DR: A new approach to solve the problem of identifying a proper list of medical equipment that requires replacement and then to optimize this list is presented by integrating Quality Function Deployment (QFD) and Genetic Algorithm (GA) in one framework.
Journal ArticleDOI
A column generation-based algorithm for midterm nurse scheduling with specialized constraints, preference considerations, and overtime
Jia Guo,Jonathan F. Bard +1 more
TL;DR: In this article, an augmented mixed-integer linear programming (MILP) formulation for the nurse scheduling problem designed to accommodate overtime and several practical considerations that are part of the rostering process was presented.
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.