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Genetic Algorithms for Selected Logistics Problems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F03%3A00007544" target="_blank" >RIV/60461373:22340/03:00007544 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Genetic Algorithms for Selected Logistics Problems

  • Original language description

    The purpose of this contribution is to show that methods of AI, genetic algorithms in particular, are very efficient tools for solving problems of optimization of serial multiproduct batch plant sequencing. This work deals with the problem of finding sequence of batches that minimizes the makespan, and discusses the application of different genetic algorithms to find such optimum sequence for a flowshop topology of a batch process. This paper presents the analysis of performance of different algorithm configurations and parameter values. The results obtained using genetic algorithms are compared to those obtained using MINLP.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    CI - Industrial chemistry and chemical engineering

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2003

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Transport & Logistics

  • ISSN

    1451-107X

  • e-ISSN

  • Volume of the periodical

    3

  • Issue of the periodical within the volume

    EX

  • Country of publishing house

    YU -

  • Number of pages

    4

  • Pages from-to

    152-155

  • UT code for WoS article

  • EID of the result in the Scopus database