A Multi-Agent Decision Support System for Supply Chain Management

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

@PhdThesis{Kovalchuk:thesis,
  author =       "Yevgeniya Kovalchuk",
  title =        "A Multi-Agent Decision Support System for Supply Chain
                 Management",
  school =       "School of Computer Science and Electronic Engineering,
                 University of Essex",
  year =         "2009",
  address =      "UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://privatewww.essex.ac.uk/~yvkova/Papers/thesisYKovalhuk.pdf",
  size =         "166 pages",
  abstract =     "Supply Chain Management (SCM) involves a number of
                 activities from negotiating with suppliers to competing
                 for customer orders and scheduling the manufacturing
                 process and delivery of goods. The activities are
                 different in their nature: they work with various data,
                 have different tasks and constraints. At the same time,
                 they are interrelated to ensure the achievement of the
                 ultimate goal of maximising the enterprise's profit.
                 This makes the chain very difficult to manage; being
                 successful in one of its areas does not necessarily
                 guarantee the improvement of the overall
                 performance.

                 Designing an effective decision-support system for SCM
                 has become crucial in recent years. With the advent of
                 e-Commerce and in a global economy, SCM systems have to
                 be able to deal with the uncertainty and volatility of
                 modern markets. In such systems, the ability to learn
                 and adapt to new conditions in the environment is of
                 paramount importance. This thesis proposes a reusable
                 demand-driven SCM solution. It introduces a number of
                 algorithms for solving different tasks across the
                 supply chain, including: constraint satisfaction,
                 prediction, planning, and online adjustments.

                 The main contribution of the thesis lies in exploring
                 the problem of dynamic pricing and predicting on line
                 auctions in the context of SCM. A number of algorithms
                 for modelling competitors' behaviour and predicting
                 customer order prices are proposed and compared in the
                 thesis. Their influence on the overall system
                 performance is also explored. The algorithms are based
                 on the Neural Networks and Genetic Programming learning
                 techniques.

                 The Trading Agent Competition SCM game is used as a
                 testbed for analysing the proposed methods. Although
                 only the results from testing the algorithms in this
                 simulated environment are provided, the methods are not
                 tied to the game rules and can successfully be used for
                 predicting other financial instruments and in other
                 competitive trading environments.",
}

Genetic Programming entries for Yevgeniya Kovalchuk

Citations