• DocumentCode
    2830504
  • Title

    Agent-based Pickup and Delivery Planning: The Learnable Evolution Model Approach

  • Author

    Wojtusiak, Janusz ; Warden, Tobias ; Herzog, Otthein

  • Author_Institution
    Machine Learning & Inference Lab., George Mason Univ., Fairfax, VA, USA
  • fYear
    2011
  • fDate
    June 30 2011-July 2 2011
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The Dynamic Vehicle Routing Problem (DVRP) is an optimization problem in which agents deliver orders that are not known in advance to the routing. Partial solutions need to be adapted to continuously accommodate new orders within dynamically changing conditions. This research focuses on using a combination of multiagent-based autonomous control with non-Darwinian evolutionary optimization. In order to compile transport plans and render optimized decisions agents managing transport vehicles employ a guided evolutionary computation method, called the learnable evolution model (LEM). Implementation and experimental evaluation of the method is performed within the Plasma multiagent simulation platform.
  • Keywords
    evolutionary computation; logistics; DVRP; agent-based pickup planning; delivery planning; dynamic vehicle routing problem; learnable evolution model approach; logistic network; multiagent based autonomous control; non-Darwinian evolutionary optimization; partial solution; Containers; Cost accounting; Equations; Logistics; Optimization; Planning; Skeleton; Autono-mous Logistics; Evolutionary Computation; Learnable Evolution Model; Multiagent-based Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    978-1-61284-709-2
  • Electronic_ISBN
    978-0-7695-4373-4
  • Type

    conf

  • DOI
    10.1109/CISIS.2011.11
  • Filename
    5989011