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Genetic algorithms in forecasting of Internet shops demand

Author

Listed:
  • Chodak, Grzegorz

Abstract

The general aim of this article is to present genetic algorithms as a tool, that can be used in de-mand forecasting in internet shops. First part of article identities factors, which have to be taken into consideration during analysing demand in internet shops, e.g. dispersion of demand, delivery time in-fluence and different e-marketing factors. Specific form of used demand function is shown in the next section of the article. Then genetic algorithm is defined by its genetic operators acting on bit strings (examples of the operators are: crossover, inversion, and mutation) and its method of credit allocation (fitness evaluation and selection). Next the method of identification of the function parameters using genetic algorithms is shown. The next part of article shows appliance of presented genetic algorithm. The advantages and disadvantages of proposed method are shortly discussed in summary.

Suggested Citation

  • Chodak, Grzegorz, 2009. "Genetic algorithms in forecasting of Internet shops demand," MPRA Paper 34034, University Library of Munich, Germany, revised 2009.
  • Handle: RePEc:pra:mprapa:34034
    as

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    File URL: https://mpra.ub.uni-muenchen.de/34034/1/MPRA_paper_34034.pdf
    File Function: original version
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    References listed on IDEAS

    as
    1. Chodak, Grzegorz & Kwaśnicki, Witold, 2000. "Genetic algorithms in seasonal demand forecasting," MPRA Paper 34099, University Library of Munich, Germany.
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    Cited by:

    1. Chodak, Grzegorz & Latus, Łukasz, 2011. "Metody prognozowania popytu i zarządzanie gospodarką magazynową w polskich sklepach internetowych – wyniki badań
      [Methods of Demand Forecasting and Inventory Management in Polish Internet Shops – R
      ," MPRA Paper 36713, University Library of Munich, Germany.

    More about this item

    Keywords

    abc analysis; inventory control; internet shop; e-commerce;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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