Emergency logistics resource scheduling algorithm in cloud computing environment
In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assig...
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| Published in: | Physical communication Vol. 64; p. 102340 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.06.2024
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| Subjects: | |
| ISSN: | 1874-4907 |
| Online Access: | Get full text |
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| Summary: | In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. Cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge Computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio. |
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| ISSN: | 1874-4907 |
| DOI: | 10.1016/j.phycom.2024.102340 |