<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper first analyzes the characteristics and principles of the layout planning of emergency resources, explores the problems of emergency resource layout and distribution planning, and mentions the multi-constraint conditions of layout planning. Then, by describing the layout planning problem of emergency resources under multi-constraints and the related variable symbols, we constructed a two-layer layout planning model of emergency resources under multi-constraints, and after analyzing the particle swarm algorithm, we designed the layout planning model solving process based on particle swarm optimization. Finally, by constructing an emergency resource layout case, the centrality of the emergency resource layout network is explored, and the shortest distance and the best site selection of each emergency resource point corresponding to the demand point are divided. The results show that the structural degree centrality is between [0,0.78], the mileage degree centrality is between [0,1], the flow degree centrality is between [0.1,1], the structural median is between [0,0.32], the mileage median is between [0,2], and the structural proximity centrality and the mileage proximity centrality scores are both within the range of [0,1]. The shortest distribution distance of A, B, C, D, and E is selected to be only 393886m, and the error with the actual is around 0.009, which is able to carry out the layout planning effectively.</p>
</abstract>

This paper first analyzes the characteristics and principles of the layout planning of emergency resources, explores the problems of emergency resource layout and distribution planning, and mentions the multi-constraint conditions of layout planning. Then, by describing the layout planning problem of emergency resources under multi-constraints and the related variable symbols, we constructed a two-layer layout planning model of emergency resources under multi-constraints, and after analyzing the particle swarm algorithm, we designed the layout planning model solving process based on particle swarm optimization. Finally, by constructing an emergency resource layout case, the centrality of the emergency resource layout network is explored, and the shortest distance and the best site selection of each emergency resource point corresponding to the demand point are divided. The results show that the structural degree centrality is between [0,0.78], the mileage degree centrality is between [0,1], the flow degree centrality is between [0.1,1], the structural median is between [0,0.32], the mileage median is between [0,2], and the structural proximity centrality and the mileage proximity centrality scores are both within the range of [0,1]. The shortest distribution distance of A, B, C, D, and E is selected to be only 393886m, and the error with the actual is around 0.009, which is able to carry out the layout planning effectively.

Emergency Resource Layout Planning Methodology with Multiple Constraints

Xi’an University of Architecture and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Emergency Resource Layout Planning Methodology with Multiple Constraints"}

This paper first analyzes the characteristics and principles of the layout planning of emergency resources, explores the problems of emergency resource layout...

Emergency Resource Layout Planning Methodology with Multiple Constraints

Pubblicato online: 05 dic 2023

Pagine: -

Ricevuto: 29 gen 2023

Accettato: 15 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01356

Parole chiave
Multiple constraints, Two-tier layout planning, Particle swarm algorithm, Emergency resource layout, Distribution planning

© 2023 Jing Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Emergency Resource Layout Planning Methodology with Multiple Constraints

Pubblicato online: 05 dic 2023

Pagine: -

Ricevuto: 29 gen 2023

Accettato: 15 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01356

Parole chiaveMultiple constraints, Two-tier layout planning, Particle swarm algorithm, Emergency resource layout, Distribution planning

© 2023 Jing Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Multiple constraints, Two-tier layout planning, Particle swarm algorithm, Emergency resource layout, Distribution planning