Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief

Abstract

PurposeThe 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.Design/methodology/approachDifferent 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.FindingsA 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.Research limitations/implicationsThe 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.Practical implicationsConsidering 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.Originality/value(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.

Robust optimization for casualty scheduling considering injury deterioration and point-edge mixed failures in early stage of post-earthquake relief

“…Cheng et al ( 23 ) adopted a two-stage robust optimization framework to study the robust fixed cost location problem in the case of uncertain demand and facility disruption, and developed a column constraint generation algorithm to accurately solve the model. Zhou et al ( 46 ) constructed a location-allocation model for emergency facilities suitable for the initial stage of post-earthquake rescue, considering facility disruption and multiple types of fuzzy demand. Mohammadi et al ( 47 ) studied a multi-objective reliable optimization model to organize a humanitarian relief chain.…”

“…Referring to literature ( 46 ), four failure scenarios are set in Supplementary Table S4 . The failure scenarios are set as follows.…”

Research on Optimization of Medical Waste Emergency Disposal Transportation Network for Public Health Emergencies in the Context of Intelligent Transportation

“…From the perspective of uncertainty, Huo et al [18] built a multi-cycle logistics network model of medical waste under fuzzy environment with minimum economic cost and maximum social benefit. Zhou et al [19] used the expected value formula of triangular fuzzy number to de-fuzzily fuzzy demand, thereby constructing the site-allocation model of emergency facilities. In addition, some scholars took into account the uncertainty of factors, such as production quantity and demand, and they adopted methods like possibility theory, fuzzy theory, grey set theory, and rough set theory to solve the problem [20,21].…”