A Location-Allocation Problem of Emergency Facilities Considering Multi-Resources Under Uncertainty

Due to the suddenness of disasters and the presence of multiple uncontrollable factors, such as the number of casualties, the availability of emergency relief supplies, and the responsiveness of hospitals, it is crucial to ensure the timely delivery of emergency relief supplies and the promptness of...

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Vydáno v:IEEE International Conference on Networking, Sensing and Control (Online) s. 1 - 6
Hlavní autoři: Cheng, Wei, Jia, Tao, Du, Ruojun, Lei, Dong
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 18.10.2024
Témata:
Approximation algorithms
Approximation methods
Disasters
Emergency facility location-allocation problem
Hospitals
Kernel search
Outer approximation method
Roads
Search problems
Sensors
Sorting
Stochastic processes
Stochastic programming
Uncertainty
ISSN:2766-8665
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Shrnutí:Due to the suddenness of disasters and the presence of multiple uncontrollable factors, such as the number of casualties, the availability of emergency relief supplies, and the responsiveness of hospitals, it is crucial to ensure the timely delivery of emergency relief supplies and the promptness of medical services under conditions of significant uncertainty. Therefore, this paper, based on the myriad uncertainties caused by disasters, adopts a priori optimization methods, incorporates multiple random variables and discrete stochastic scenarios to enhance model reliability, and proposes a two-stage stochastic model for the location-allocation of emergency facilities. This model is further transformed into an equivalent deterministic model based on discrete scenarios, and integrates queueing theory to calculate the casualties' waiting penalty cost. For solving the model, this paper first employs an outer approximation method to simplify the nonlinear parts of the model, facilitating the resolution of small-scale problems; for large-scale problems, a Kernel search algorithm is proposed, enhanced by a sorting algorithm. Finally, the effectiveness of the model and algorithms is validated through test instances.
ISSN:2766-8665
DOI:10.1109/ICNSC62968.2024.10760233