Location Problems Optimization by a Self-Organizing Multiagent Approach

Sana Moujahed Olivier Simonin 1 Abderrafiaa Koukam
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The Facility Location Problem (FLP) requires locating facilities in order to optimize some performance criteria. This problem occurs in many practical settings where facilities provide a service, such as the location of plants, bus-stops, fire stations, etc. Particularly, we deal with the continuous version of location problem where facilities have to be located in an Euclidean plane. This paper contributes to research on location problems by exploring a new approach based on reactive multiagent systems. The proposed model relies on a set of agents situated in a common environment which interact and attempt to reach a global optimization goal. The interactions between agents and their environment, which are based on the artificial potential fields approach, allow to locally optimize the agent's locations. The optimization of the whole system is the outcome of a process of agents self-organization. Then, we present how the model can be extended to the multi-level version of the location problem. Finally, the approach is evaluated to check its relevance. These evaluations concern both presented versions of the location problem.
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Multiagent and Grid Systems - An International Journal of Cloud Computing , IOS Press, 2009, Special Issue on Engineering Environments For Multiagent Systems, 5 (1), pp.59-74. 〈10.3233/MGS-2009-0119〉
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https://hal.inria.fr/inria-00172337
Contributeur : Olivier Simonin <>
Soumis le : vendredi 14 septembre 2007 - 19:31:37
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51

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Sana Moujahed, Olivier Simonin, Abderrafiaa Koukam. Location Problems Optimization by a Self-Organizing Multiagent Approach. Multiagent and Grid Systems - An International Journal of Cloud Computing , IOS Press, 2009, Special Issue on Engineering Environments For Multiagent Systems, 5 (1), pp.59-74. 〈10.3233/MGS-2009-0119〉. 〈inria-00172337〉

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