Deriving inventory-control policies with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Peer Kleinau and Ulrich W. Thonemann",
  title =        "Deriving inventory-control policies with genetic
  journal =      "OR Spectrum",
  year =         "2004",
  volume =       "26",
  number =       "4",
  pages =        "521--546",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Inventory
                 control, Supply chain management",
  ISSN =         "0171-6468",
  DOI =          "doi:10.1007/s00291-004-0159-5",
  abstract =     "One of the key areas of operations and supply chain
                 management is inventory control. Inventory control
                 determines which quantity of a product should be
                 ordered when to achieve some objective, such as
                 minimising cost. Inventory-control policies are
                 typically derived analytically, and this requires
                 advanced mathematical skills and can be quite time
                 consuming. In this paper, we present an alternative
                 approach for solving inventory-control problems that is
                 based on Genetic Programming. Genetic Programming is an
                 optimisation method that applies the principles of
                 natural evolution to optimization problems. One of the
                 key characteristics of Genetic Programming is that it
                 does not require the specification of how a problem
                 should be solved, but only the specification of what
                 needs to be solved. After the user has specified the
                 problem, GP searches for a solution without significant
                 human involvement. The solutions generated by GP can be
                 simple algorithms or closed-form expressions that
                 represent the decision variables, i.e., the order point
                 and the order quantity as a function of the problem
                 parameters. However, expert knowledge in inventory
                 control is still essential for building the inventory
                 models and determining the parameters of Genetic
                 Programming. Genetic Programming searches for both the
                 structure and the parameters of the optimal solution.
                 For simple settings, the structure and the parameters
                 of the optimal solution can be found. For complex
                 settings, near-optimal solutions that outperform
                 traditional heuristics can be found if the structure of
                 the optimal solution is known.",
  notes =        "",

Genetic Programming entries for Peer Kleinau Ulrich Thonemann