Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers objectives and nurses preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics a differential evolution algorithm (DE) and a greedy randomised adaptive search procedure (GRASP) to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP
The Nurse Scheduling Problem (NSP) is a combination of optimization problem and important manageme...
In this paper, we propose a search technique for nurse scheduling, which deals with it as a multi-ob...
Since the Nurse Scheduling Problem (NSP) is mostly made up of numerous restrictions and assumption...
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem ...
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem ...
In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NS...
In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NS...
The Nurse scheduling problem (NSP) represents a difficult class of Multi-objective optimization prob...
When applying evolutionary algorithms to difficult real-world problems, the fitness function routine...
Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all...
We applied genetic algorithm to nurse scheduling problem. For time complexity problem of genetic alg...
An effective and efficient nurse work schedule could fulfill nurses’ work satisfaction. It certainly...
Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all...
This paper describes a Genetic Algorithms (GAs) approach to a manpower-scheduling problem arising at...
Motivated by the biological metamorphosis process and the need to solve multi-objective optimization...
The Nurse Scheduling Problem (NSP) is a combination of optimization problem and important manageme...
In this paper, we propose a search technique for nurse scheduling, which deals with it as a multi-ob...
Since the Nurse Scheduling Problem (NSP) is mostly made up of numerous restrictions and assumption...
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem ...
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem ...
In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NS...
In this paper, we present a hybrid genetic algorithm for the well-known nurse scheduling problem (NS...
The Nurse scheduling problem (NSP) represents a difficult class of Multi-objective optimization prob...
When applying evolutionary algorithms to difficult real-world problems, the fitness function routine...
Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all...
We applied genetic algorithm to nurse scheduling problem. For time complexity problem of genetic alg...
An effective and efficient nurse work schedule could fulfill nurses’ work satisfaction. It certainly...
Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all...
This paper describes a Genetic Algorithms (GAs) approach to a manpower-scheduling problem arising at...
Motivated by the biological metamorphosis process and the need to solve multi-objective optimization...
The Nurse Scheduling Problem (NSP) is a combination of optimization problem and important manageme...
In this paper, we propose a search technique for nurse scheduling, which deals with it as a multi-ob...
Since the Nurse Scheduling Problem (NSP) is mostly made up of numerous restrictions and assumption...