Optimal decision of a disaster relief network equilibrium model
Frequent natural disasters challenge relief network efficiency. This paper introduces a stochastic relief network with limited path capacity, develops an equilibrium model based on cumulative prospect theory, and formulates it as a stochastic variational inequality problem to enhance emergency respo...
| Published in: | AIMS Mathematics |
|---|---|
| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
American Institute of Mathematical Sciences
2024
|
| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180690768&doi=10.3934%2fmath.2024131&partnerID=40&md5=7a015d8660497392b401e077cf397eaf |
| id |
2-s2.0-85180690768 |
|---|---|
| spelling |
2-s2.0-85180690768 Li C.; Zhang W.; Yee H.M.; Yang B. Optimal decision of a disaster relief network equilibrium model 2024 AIMS Mathematics 9 2 10.3934/math.2024131 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180690768&doi=10.3934%2fmath.2024131&partnerID=40&md5=7a015d8660497392b401e077cf397eaf Frequent natural disasters challenge relief network efficiency. This paper introduces a stochastic relief network with limited path capacity, develops an equilibrium model based on cumulative prospect theory, and formulates it as a stochastic variational inequality problem to enhance emergency response and resource allocation efficiency. Using the NCP function, Lagrange function, and random variables, the model dynamically monitors disasters, enabling rational resource allocation for quick decision-making. Compared to traditional methods, our model significantly improves resource scheduling and reduces disaster response costs. Through a random network example, we validate the model’s effectiveness in aiding intelligent decision-making for relief plans and resource allocation optimization. © 2024 the Author(s), licensee AIMS Press. American Institute of Mathematical Sciences 24736988 English Article All Open Access; Gold Open Access |
| author |
Li C.; Zhang W.; Yee H.M.; Yang B. |
| spellingShingle |
Li C.; Zhang W.; Yee H.M.; Yang B. Optimal decision of a disaster relief network equilibrium model |
| author_facet |
Li C.; Zhang W.; Yee H.M.; Yang B. |
| author_sort |
Li C.; Zhang W.; Yee H.M.; Yang B. |
| title |
Optimal decision of a disaster relief network equilibrium model |
| title_short |
Optimal decision of a disaster relief network equilibrium model |
| title_full |
Optimal decision of a disaster relief network equilibrium model |
| title_fullStr |
Optimal decision of a disaster relief network equilibrium model |
| title_full_unstemmed |
Optimal decision of a disaster relief network equilibrium model |
| title_sort |
Optimal decision of a disaster relief network equilibrium model |
| publishDate |
2024 |
| container_title |
AIMS Mathematics |
| container_volume |
9 |
| container_issue |
2 |
| doi_str_mv |
10.3934/math.2024131 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180690768&doi=10.3934%2fmath.2024131&partnerID=40&md5=7a015d8660497392b401e077cf397eaf |
| description |
Frequent natural disasters challenge relief network efficiency. This paper introduces a stochastic relief network with limited path capacity, develops an equilibrium model based on cumulative prospect theory, and formulates it as a stochastic variational inequality problem to enhance emergency response and resource allocation efficiency. Using the NCP function, Lagrange function, and random variables, the model dynamically monitors disasters, enabling rational resource allocation for quick decision-making. Compared to traditional methods, our model significantly improves resource scheduling and reduces disaster response costs. Through a random network example, we validate the model’s effectiveness in aiding intelligent decision-making for relief plans and resource allocation optimization. © 2024 the Author(s), licensee AIMS Press. |
| publisher |
American Institute of Mathematical Sciences |
| issn |
24736988 |
| language |
English |
| format |
Article |
| accesstype |
All Open Access; Gold Open Access |
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1809677576056078336 |