The purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.
Different from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.
A case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.
The casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.
Considering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.
(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.
This research is funded by Chongqing Social Science Planning project (No.2021NDYB070); National Natural Science Foundation of China (No.71702015); The Ministry of Education of Humanities and Social Science project of China (No.21YJCZH249); Natural Science Foundation of Chongqing Municipality (No. cstc2021jcyj-msxmX0527 and cstc2021ycjh-bgzxm0136); Natural Science Foundation of Hunan Province (No. 2021JJ30195); The Research Platform Open Project in CTBU (KFJJ2017057); Science and Technology Research Projects of Chongqing Education Commission (No. KJQN201900812); Humanities and Social Sciences Research Program of Chongqing Education Commission (No. 22SKGH186) and Innovative Research Project for Graduate Students of Chongqing Technology and Business University (No. yjscxx2022-112-24).
Zhou, Y., Gong, Y., Hu, X. and Liu, C. (2022), "Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief", Grey Systems: Theory and Application, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/GS-08-2022-0090
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