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 Experiment Videos

Solving a real-world problem using an evolving heuristically driven schedule builder.

E Hart1, P Ross, J Nelson

  • 1Department of Artificial Intelligence, University of Edinburgh. emmah@dai.ed.ac.uk

Evolutionary Computation
|February 18, 1999
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Persistent headache and low back pain after accidental dural puncture in the obstetric population: a prospective, observational, multicentre cohort study.

Anaesthesia·2021
Same author

The design and use of a simple device for the MRI assessment of changes in cardiovascular function by lower-body negative-pressure-simulated reduction of central blood volume.

Clinical radiology·2021
Same author

Anterior prostate fat resection during prostatectomy: a histopathologic review.

Journal of robotic surgery·2020
Same author

Multi-year diagnosis of unpredictable fouling occurrences in a full-scale membrane bioreactor.

Water science and technology : a journal of the International Association on Water Pollution Research·2020
Same author

L-Glutamine in sickle cell disease.

Drugs of today (Barcelona, Spain : 1998)·2020
Same author

Rat Cytomegalovirus Vaccine Prevents Accelerated Chronic Rejection in CMV-Naïve Recipients of Infected Donor Allograft Hearts.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2015
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles

This study optimized chicken catching and transport schedules using a novel genetic algorithm (GA) approach. The tailored schedule builder effectively addresses complex, real-world logistics challenges, improving efficiency.

Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Logistics Management

Background:

  • Real-world logistics face complex scheduling challenges.
  • Efficiently managing live animal transport requires robust solutions.
  • Existing methods may not adequately address dynamic constraints.

Purpose of the Study:

  • To develop an optimized scheduling system for live chicken catching and transportation.
  • To improve upon traditional scheduling methods using evolutionary computation.
  • To create a flexible and robust system for dynamic logistics problems.

Main Methods:

  • Decomposition of the complex scheduling problem into two subproblems.
  • Application of separate genetic algorithms (GAs) to each subproblem.

Related Experiment Videos

  • Focus on evolving a tailored 'schedule builder' for each unique daily problem.

    • Main Results:

    • The proposed GA approach provides a robust, fast, and flexible scheduling system.
    • Evolving the schedule builder yielded superior, tailored solutions compared to traditional methods.
    • Population-based evolutionary methods outperformed hill-climbing and simulated annealing in solution quality.

    Conclusions:

    • A GA-based approach, focusing on evolving schedule builders, effectively solves complex logistics scheduling.
    • This method offers a significant improvement in solution quality and adaptability for real-world problems.
    • Population-based methods provide multiple optimal solutions, enhancing practical applicability.