Knowledge-based estimation of stockout costs in logistic systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Langton:2011:ISDA,
  author =       "Sebastian Langton and Martin Josef Geiger",
  title =        "Knowledge-based estimation of stockout costs in
                 logistic systems",
  booktitle =    "11th International Conference on Intelligent Systems
                 Design and Applications (ISDA 2011)",
  year =         "2011",
  month =        "22-24 " # nov,
  pages =        "772--777",
  address =      "Cordoba",
  size =         "6 pages",
  abstract =     "The approach introduced in this paper depicts the
                 topic of identification and evaluation of stockout
                 consequences, commonly denoted as stockout cost
                 quantification. Our work is motivated by the limited
                 number of approaches dealing with this problem and,
                 primarily in the field of inventory management, a
                 subsequent need for applicable methods providing
                 reliable stockout cost parameters. We focus on the
                 problem of estimating opportunity costs of stockouts as
                 the most difficult cost component to be determined.
                 Therefore, a method to elicit information by
                 confronting relevant decision makers with
                 representative stockout cases (a priori) is presented.
                 Subsequently, a Genetic Programming (GP) approach for
                 learning opportunity cost functions from these
                 case-based decisions is introduced. It is shown on
                 exemplary tests instances that solutions can be
                 generated which converge to structurally similar
                 opportunity cost functions for representative stockout
                 items. Based on a comparison to benchmarks generated by
                 Neural Networks, it can be concluded that the quality
                 of solutions from the GP algorithm is satisfying.",
  keywords =     "genetic algorithms, genetic programming, GP algorithm,
                 case-based decisions, decision making, genetic
                 programming approach, inventory management,
                 knowledge-based estimation, learning opportunity cost
                 functions, logistic systems, neural networks,
                 opportunity cost estimation, stockout consequence
                 identification, stockout cost quantification, costing,
                 decision making, inventory management, knowledge based
                 systems, logistics, neural nets, production engineering
                 computing",
  DOI =          "doi:10.1109/ISDA.2011.6121750",
  ISSN =         "2164-7143",
  notes =        "Also known as \cite{6121750}",
}

Genetic Programming entries for Sebastian Langton Martin J Geiger

Citations