In order to achieve rapid and fair distribution of emergency supplies after a large-scale sudden disaster, this paper constructs a comprehensive time perception satisfaction function and a comprehensive material loss pain function to portray the perceived satisfaction of disaster victims based on objective constraints such as limited transport, multimodal transport and supply being less than demand, and at the same time considers the subjective perception of time and material quantity of disaster victims under limited rational conditions, and constructs a multi-objective optimisation model for the dispatch of multi-cycle emergency supplies by combining comprehensive rescue cost information. For the characteristics of the proposed model, based on the NSGA-II algorithm, generalized reverse learning strategy, coding repair strategy, improved adaptive crossover, variation strategy, and elite retention strategy are introduced. Based on this, we use the real data of the 2008 Wenchuan earthquake combined with simulated data to design corresponding cases for validation and comparison with the basic NSGA-II algorithm, SPEA-II and MOPSO algorithms. The results show that the proposed model and algorithm can effectively solve the large-scale post-disaster emergency resource allocation problem, and the improved NSGA- II algorithm has better performance.

The prediction of casualties in earthquake disasters is a prerequisite for determining the quantity of emergency supplies needed and serves as the foundational work for the timely distribution of resources. In order to address challenges such as the large computational workload, tedious training process, and multiple influencing factors associated with predicting earthquake casualties, this study proposes a Support Vector Machine (SVM) model utilizing Principal Component Analysis (PCA) and Bayesian Optimization (BO). The original data are first subjected to dimensionality reduction using PCA, with principal components contributing cumulatively to over 80% selected as input variables for the SVM model, while earthquake casualties are designated as the output variable. Subsequently, the optimal hyperparameters for the SVM model are obtained using the Bayesian Optimization algorithm. This approach results in the development of an earthquake casualty prediction model based on PCA-BO-SVM. Experimental results indicate that compared to the GA-SVM model, the BO-SVM model, and the PCA-GA-SVM model, the PCA-BO-SVM model exhibits a reduction in average error rates by 12.86%, 9.01%, and 2%, respectively, along with improvements in average accuracy and operational efficiency by 10.1%, 7.05%, and 0.325% and 25.5%, 18.4%, and 19.2%, respectively. These findings demonstrate that the proposed PCA-BO-SVM model can effectively and scientifically predict earthquake casualties, showcasing strong generalization capabilities and high predictive accuracy.

With the arrival of an aging society, the high shortage of public medical resources leads to the increasingly serious pension problem, which has attracted extensive attention. This has led to a sharp increase in the demand for family doctors. In order to alleviate the shortage of caregivers, this paper considers the scheduling problem of home health care, which includes the synchronous access of caregivers and vehicles, and the flexible marchingmatching between caregivers and patients. First of all, we construct a Mixed Integer Linear Programming (MILP) to characterize the optimization problem. Secondly, an improved non-dominated sorting genetic algorithm with elite strategy (INSGA-II) is designed. The initial solution with high quality is obtained through the simultaneous evolution of two populations. The chromosomes are optimized by using improved crossover and mutation operators, and local search strategies are designed on this basis. Finally, several groups of test examples are used to verify the feasibility and effectiveness of the algorithm, and analyze the impact of different visit modes on objective function. The results show that the synchronous access can effectively reduce the costs, and can provide theoretical support for the medical center to make plans.

The stage of social aging and further deepening of population aging has been witnessed in China. The demand for home care is increasingly growing; meanwhile, medical human resources are insufficient. In this context, a home health caregiver scheduling problem under the resource sharing mode is studied in this paper. Under such mode, two types of caregivers, i.e., full-time caregiver and part-time caregiver, are regarded as the main labor force by HHC institutions. In HHC planning, different working times for the two kinds of caregivers will need to be considered. Consequently, in this paper, a corresponding mathematical model is established and a hybrid algorithm that combines the whale optimization algorithm (WOA) with the particle swarm optimization (PSO) algorithm is proposed to solve the model. The proposed algorithm is compared with the existing algorithms to verify its effectiveness through three example tests of different scales and Solomon example. Finally, the resource sharing model is compared with the traditional model through a case, and the rationality of home health caregiver scheduling in the resource sharing model is discussed in terms of cost structure and customer satisfaction.

To address the safety and efficiency challenges in planning emergency evacuation routes for personnel in complex environments, this study proposes an integrated and improved ant colony optimization (ACO) with a genetic algorithm (GA). First, an emergency evacuation route planning model for subway incidents is constructed by optimizing evacuation time, route risk, and the passenger panic index. Then, the ant colony algorithm is enhanced by assigning pheromones to each objective and optimizing the state transition probabilities, which helps avoid premature convergence on local optima. Simultaneously, a GA is employed to conduct a global search and generate an initial population, which serves as the initial pheromone for the ACO. This approach achieves the integration of ACO and GA, enabling them to synergistically leverage the advantages of global and local search. Finally, an evacuation simulation was conducted using a specific subway station as an example, and the results were compared with those of traditional algorithms. The results indicate that the proposed algorithm can find the optimal solution for all evacuation routes and significantly improve convergence speed and global search capabilities. In simulations across different hazard development stages, the proposed integrated method outperforms basic ACO and SSA by accounting for evacuation time, safety, and crowd panic to yield optimal routes.