• DocumentCode
    2388377
  • Title

    The research about emergency logistics distribution routing optimization based on Adaptive Ant Colony Algorithm

  • Author

    Zhang, Liyi ; Fei, Teng ; Sun, Yunshan ; Zhang, Jin ; Xu, Wenchao

  • Author_Institution
    Inf. Eng. Coll., Tianjin Univ. of Commerce, Tianjin, China
  • fYear
    2012
  • fDate
    19-20 May 2012
  • Firstpage
    767
  • Lastpage
    770
  • Abstract
    The most outstanding feature of emergency distribution is time urgency. Decision maker should use less time to complete the distribution scheme which should guarantee the materials reach the emergency site with less time. At this time it is especially important to choose the optimal path in the decision-making process. This article builds up mathematical model which is based on the emergency logistics distribution in conventional traffic (ideal traffic) condition with much vehicles, which is to reach the goal that the delivery time is shortest, and analysis the rationality and feasibility of the model. Based on this, utilize the Adaptive Ant Colony Optimization (AACO), the modern optimization theory, to optimize the model, and indicate the effectiveness on this problem with the computer simulation.
  • Keywords
    ant colony optimisation; decision making; emergency services; logistics; road traffic; road vehicles; AACO; adaptive ant colony optimization algorithm; computer simulation; conventional traffic condition; decision-making process; delivery time; distribution scheme; emergency logistics distribution routing optimization; emergency site; mathematical model; optimal path; optimization theory; time urgency; vehicle; Adaptation models; Ant colony optimization; Computational modeling; Materials; Optimization; Routing; Vehicles; Adaptive Ant Colony optimization; emergency logistics; routing optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2012 International Conference on
  • Conference_Location
    Yantai
  • Print_ISBN
    978-1-4673-0198-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2012.6223123
  • Filename
    6223123