Izvorni znanstveni članak
https://doi.org/10.21278/brod75103
Robust optimization method of emergency resource allocation for risk management in inland waterways
Quandang Ma
; Wuhan University of Technology, Wuhan, China
Zhushan Wang
; Wuhan University of Technology, Wuhan, China
Tuqiang Zhou
; East China Jiaotong University, Nanchang, China
*
Zhao Liu
; Wuhan University of Technology, Wuhan, China
* Dopisni autor.
Sažetak
This study proposes a robust optimization method for waterborne emergency resource allocation in inland waterways that addresses the uncertainties and mismatches between supply and demand. To accomplish this, we integrate the risk evaluation of maritime with a robust optimization model and employ the Entropy Weighted Method (EWM)-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-Analytic Hierarchy Process (AHP) method to evaluate the risk of various areas. The approach enables exploration of the relationship between maritime risk and emergency resource allocation strategy. The robust optimization method is used to deal with uncertainty and derive the robust counterpart of the proposed model. We establish an emergency resource allocation model that considers both the economy and timeliness of emergency resource allocation. We construct an optimization model and transform it into an easily solvable robust counterpart model. The results demonstrate that the proposed method can adapt to real-world scenarios, and effectively optimize the configuration effect while improving rescue efficiency under reasonable resource allocation. Specifically, the proportion of rescue time saved ranges from 28.52% to 92.60%, and the proportion of total cost saved is 95.82%. Our approach has significant potential to provide a valuable reference for decision-making related to emergency resource allocation in maritime management.
Ključne riječi
Maritime safety; emergency resource allocation; robust optimization; maritime risk; inland waterways
Hrčak ID:
312937
URI
Datum izdavanja:
1.1.2024.
Posjeta: 657 *