Intelligent Facility Layout Planning for Emergency Department: A Multi-Agent System and Particle Swarm Optimization Approach

The efficient design of facility layouts for emergency services represents a major challenge due to the dynamic and complex nature of their operations. Within this paper, a fresh approach is introduced to tackle this particular challenge, drawing upon the collaborative power of multi-agent systems a...

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Bibliographic Details
Published in2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) pp. 1 - 7
Main Authors Bouramtane, Khalil, Kharraja, Said, Riffi, Jamal, Elbeqqali, Omar, Boujraf, Said
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.11.2023
Subjects
Collaboration
Complexity theory
Costs
Dynamic Environments
Emergency services
Facility Layout Optimization
Intelligent Layout Design
Layout
Multi-Agent Systems
Particle swarm optimization
Planning
Simulation
Workflow Efficiency
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Summary:The efficient design of facility layouts for emergency services represents a major challenge due to the dynamic and complex nature of their operations. Within this paper, a fresh approach is introduced to tackle this particular challenge, drawing upon the collaborative power of multi-agent systems along with the principles of particle swarm optimization. Moreover, the utilization of simulation technique is incorporated to enrich the comprehension and depiction of the analyzed system.In the proposed framework, agents representing different entities within the emergency department collaborate through a particle swarm optimization algorithm, working collectively towards an optimal facility layout. The main aim is to minimize travel costs and improve workflow efficiency. The results highlight the superiority of the multi-agent particle swarm optimization approach in the realization of intelligent layouts for emergency services. This research contributes to the advancement of facility planning by providing an innovative and efficient solution applicable to dynamic operational environments.
DOI:10.1109/SITA60746.2023.10373758