• A load balancing module for po...

Result: A load balancing module for post-emergency management

Title:
A load balancing module for post-emergency management
Authors:
KOLOMVATSOS, Kostas, PANAGIDI, Kyriaki, HADJIEFTHYMIADES, Stathes
Source:
Expert systems with applications. 42(1):657-667
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Théorie du risque. Assurance, Risk theory. Actuarial science, Gestion des stocks, gestion de la production. Distribution, Inventory control, production control. Distribution, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Sciences biologiques et medicales, Biological and medical sciences, Sciences medicales, Medical sciences, Sante publique. Hygiene-medecine du travail, Public health. Hygiene-occupational medicine, Santé publique. Hygiène, Public health. Hygiene, Généralités, General aspects, Planification. Prévention (méthodes). Intervention. Evaluation, Planification. Prevention (methods). Intervention. Evaluation, Allocation optimale, Optimal allocation, Asignación óptima, Equilibrage charge, Load balancing, Equilibrio de carga, Gestion crise, Crisis management, Gestión crisis, Guidage, Guidance, Guiado, Hôpital, Hospital, Installation extérieure, Outdoor installation, Instalación exterior, Installation intérieure, Indoor installation, Instalación interior, Modélisation, Modeling, Modelización, Responsabilité, Responsibility, Responsabilidad, Sauvetage, Rescue, Salvamento, Scénario, Script, Argumento, Sinistre, Disaster, Siniestro, Urgence, Emergency, Urgencia, Emergency management, Prediction
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28843432
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Operational research. Management

Public health. Hygiene-occupational medicine. Information processing
Accession Number:
edscal.28843432
Database:
PASCAL Archive

Further Information

Research society has developed a number of models and tools to support emergency management. The proposed models are mainly designed for indoor applications oriented to provide guidance directly to people in danger. Only a few of them deal with outdoor scenarios as well as with providing directions to field commanders or rescue teams. Additionally, load balancing techniques for the optimal allocation of a number of entities into a number of resources are understudied creating a gap in the corresponding research. In this paper, we propose a load balancing model oriented to assist field commanders and rescue teams in a post-emergency scenario. The proposed system could be applied either for indoor or outdoor applications. The module builds on top of the solution provided for the known Santa Fe Bar Problem (SFBP). It consists of an intelligent technique aiming to distribute a number of entities into a finite number of resources. A set of predictors undertake the responsibility of estimating the load of each resource. These predictors are adopted to select the appropriate resource for each entity. A case study deals with the distribution of injured persons into a number of hospitals and presents the functionality of the proposed module. Finally, numerical results reveal computational and time requirements of our system.