Int. J. of Advanced Intelligence Paradigms   »   2012 Vol.4, No.1

 

 

Title: A hybrid modelling framework to simulate disaster response decisions

 

Authors: Shengnan Wu; Larry J. Shuman; Bopaya Bidanda; Carey D. Balaban; Ken Sochats

 

Addresses:
Department of Operations Research and Decision Support, American Airlines, 4333 Amon Carter Blvd. MD 5358, Fort Worth, TX 76155, USA.
Department of Industrial Engineering, Swanson School of Engineering, University of Pittsburgh, 323 Benedum Hall, 3700 O’Hara St., Pittsburgh, PA 15261, USA.
Department of Industrial Engineering, Swanson School of Engineering, University of Pittsburgh, 1048 Benedum Hall, 3700 O’Hara St., Pittsburgh, PA 15261, USA.
Department of Otolaryngology, Neurobiology, Communication Sciences and Disorders, and Bioengineering, Center for National Preparedness, University of Pittsburgh, 107 Eye and Ear Institute, 203 Lothrop Street, Pittsburgh, PA 15213, USA.
Department of Information Science and Telecommunications, Visual Information Systems Center, Center for National Preparedness, University of Pittsburgh, 707 SIS, Pittsburgh, PA 15260, USA

 

Abstract: Computer simulations are designed to imitate detailed system operations. They are utilised to evaluate system performance, predict future behaviour, or compare various policy alternatives. Two main streams exist in current simulation research and practise: discrete event simulation (DES) and agent-based simulation (ABS). This paper describes a unique simulation modelling scheme to combine DES and ABS on one integrated platform in order to take advantages of and eliminate the disadvantages of both methods. The hybrid simulation system was designed and developed to study various disaster response problems. Its successful modelling of a major US city proves the capability of the hybrid method for constructing efficient, flexible large-scale simulations.

 

Keywords: discrete event simulation; DES; agent-based simulation; ABS; hybrid simulation; disaster response; geographic information systems; GIS; rule-based systems; agent-based systems; multi-agent systems; emergency management; disaster management; modelling.

 

DOI: 10.1504/IJAIP.2012.046968

 

Int. J. of Advanced Intelligence Paradigms, 2012 Vol.4, No.1, pp.83 - 102

 

Available online: 21 May 2012

 

 

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