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

An evolutionary algorithm for interval solid transportation problems.

F Jiménez1, J L Verdegay

  • 1Departmento de Informática, Inteligencia Artificial y Electrónica, Facultad de Informatica, Universidad de Murcia, Campus de Espinardo, 30071-Espinardo, Murcia, Spain. fernan@dif.um.es

Evolutionary Computation
|April 13, 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

Long-hair FUE: advantages and disadvantages of the most recent technique in hair transplantation.

Actas dermo-sifiliograficas·2026
Same author

Design of more efficient luminescent solar concentrators by using peripherally dye-doped stacked optical fibers.

Optics express·2023
Same author

Laser capture microdissection as a method for investigating the human hair follicle microbiome reveals region-specific differences in the bacteriome profile.

BMC research notes·2023
Same author

Changes in electromyographic activity of latent trigger points after a dry needling intervention: a randomised controlled trial.

Physiotherapy·2022
Same author

Authors' reply to the comment on "Effects of dry needling on mechanical and contractile properties of the upper trapezius with latent myofascial trigger points: A randomized controlled trial".

Musculoskeletal science & practice·2022
Same author

Effects of dry needling on mechanical and contractile properties of the upper trapezius with latent myofascial trigger points: A randomized controlled trial.

Musculoskeletal science & practice·2021
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 addresses the Solid Transportation Problem with interval data for sources, destinations, and conveyances. An Evolutionary Algorithm is proposed to solve these complex problems with linear or nonlinear objectives.

Area of Science:

  • Operations Research
  • Optimization Theory

Background:

  • The Solid Transportation Problem (STP) typically involves fixed values for sources, destinations, and transport modes.
  • Real-world transportation scenarios often involve uncertainty or variability in these parameters, necessitating interval-based modeling.

Purpose of the Study:

  • To extend the Solid Transportation Problem to accommodate interval data for sources, destinations, and conveyances.
  • To develop a generalized method for solving STPs with interval constraints and arbitrary objective functions.

Main Methods:

  • Formulation of the Solid Transportation Problem with interval data for all key parameters.
  • Development and application of an Evolutionary Algorithm tailored for interval STPs.
  • Generalization of existing methods that handle only point-valued data.

Related Experiment Videos

Main Results:

  • The proposed Evolutionary Algorithm effectively handles STPs with interval data.
  • The method provides a generalized approach applicable to a wider range of transportation problems.
  • Demonstrated ability to solve problems with both linear and nonlinear objective functions.

Conclusions:

  • Interval data representation is crucial for realistic Solid Transportation Problems.
  • The proposed Evolutionary Algorithm offers a robust and flexible solution methodology for interval STPs.
  • This research advances the field of transportation optimization by incorporating interval uncertainty.