Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Optimal Foraging00:48

Optimal Foraging

14.1K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.1K
Altruism01:03

Altruism

47.9K
Altruistic behaviors are “unselfish” behaviors—those that help another individual at the expense of the individual carrying out the behavior. Despite the negative consequences for the altruistic animal, these behaviors are thought to have evolved for several reasons.
47.9K
Cluster Sampling Method01:20

Cluster Sampling Method

15.2K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN).

IEEE access : practical innovations, open solutions·2021
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
See all related articles

Related Experiment Video

Updated: Mar 3, 2026

Preparation of Single-cohort Colonies and Hormone Treatment of Worker Honeybees to Analyze Physiology Associated with Role and/or Endocrine System
08:53

Preparation of Single-cohort Colonies and Hormone Treatment of Worker Honeybees to Analyze Physiology Associated with Role and/or Endocrine System

Published on: September 6, 2016

16.4K

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem.

M Rajeswari1, J Amudhavel2, Sujatha Pothula1

  • 1Department of CSE, Pondicherry University, Puducherry, India.

Computational Intelligence and Neuroscience
|May 6, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new metaheuristic algorithm, the Directed Bee Colony Optimization Algorithm, to solve the complex Nurse Rostering Problem (NRP). The novel approach effectively optimizes nurse scheduling by balancing constraints.

More Related Videos

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model
10:58

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

Published on: August 29, 2013

11.7K
A Push-pull Protocol to Reduce Colonization of Bird Nest Boxes by Honey Bees
06:03

A Push-pull Protocol to Reduce Colonization of Bird Nest Boxes by Honey Bees

Published on: September 4, 2016

9.1K

Related Experiment Videos

Last Updated: Mar 3, 2026

Preparation of Single-cohort Colonies and Hormone Treatment of Worker Honeybees to Analyze Physiology Associated with Role and/or Endocrine System
08:53

Preparation of Single-cohort Colonies and Hormone Treatment of Worker Honeybees to Analyze Physiology Associated with Role and/or Endocrine System

Published on: September 6, 2016

16.4K
Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model
10:58

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

Published on: August 29, 2013

11.7K
A Push-pull Protocol to Reduce Colonization of Bird Nest Boxes by Honey Bees
06:03

A Push-pull Protocol to Reduce Colonization of Bird Nest Boxes by Honey Bees

Published on: September 4, 2016

9.1K

Area of Science:

  • Operations Research
  • Computer Science
  • Healthcare Management

Background:

  • The Nurse Rostering Problem (NRP) is an NP-hard combinatorial optimization challenge.
  • Efficiently assigning nurses to shifts requires balancing numerous hard and soft constraints.
  • Existing methods often struggle with the complexity and scale of real-world NRP instances.

Purpose of the Study:

  • To propose a novel metaheuristic technique for solving the Nurse Rostering Problem.
  • To develop and adapt a Multiobjective Directed Bee Colony Optimization (MODBCO) algorithm for NRP.
  • To demonstrate the efficacy of MODBCO in optimizing nurse scheduling.

Main Methods:

  • A multiobjective mathematical programming model was employed.
  • A Directed Bee Colony Optimization Algorithm integrated with the Modified Nelder-Mead Method was developed.
  • The MODBCO algorithm combines deterministic local search, multiagent particle systems, and bee decision-making principles.

Main Results:

  • The MODBCO algorithm successfully solved the multiobjective optimization problem for nurse scheduling.
  • Performance was evaluated using the standard INRC2010 dataset, reflecting diverse real-world scenarios.
  • Statistical analysis confirmed the algorithm's unique performance on assessment criteria.

Conclusions:

  • The proposed MODBCO algorithm offers an effective metaheuristic solution for the Nurse Rostering Problem.
  • This approach demonstrates strong performance across varying problem sizes and complexities.
  • The integration of diverse optimization techniques enhances the algorithm's capability for complex scheduling tasks.