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
Nurse (Re)scheduling via answer set programming
Journal ArticleDOI
Multi-objective Nurse Scheduling Models with Patient Workload and Nurse Preferences
TL;DR: Results show that considering patient workload in the optimization models can make positive impacts in nurse scheduling by keeping higher nurse job satisfaction and min imizing unsatisfied patient workloads.
Solving Combinatorial Optimization Problems Using Genetic Algorithms and Ant Colony Optimization
TL;DR: An approach that utilizes a genetic algorithm and discrete event simulation to solve the physician scheduling problem in a hospital is proposed and is tested on real world datasets for physician schedules.
Journal ArticleDOI
Modelling a nurse shift schedule with multiple preference ranks for shifts and days-off
TL;DR: A nurse scheduling model based upon integer programming that takes into account constraints of the schedule, different preference ranks towards each shift, and the historical data of previous schedule periods to maximize the satisfaction of all the nursing staff's preferences about the shift schedule is proposed.
Proceedings ArticleDOI
Nurse scheduling using binary fuzzy goal programming
Ece Cetin,Ahmet Sarucan +1 more
TL;DR: A multi objective integer programming model for NSP is proposed and then based on this model, Chang' binary fuzzy goal programming approach is applied to the problem.
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.