Search

In recent years, the need for emergency resources has dramatically increased and it has caused an overcrowding problem for the emergency department (ED). Solving this problem by increasing medical resources is either impractical or infeasible. Thus, this manuscript develops a multi-objective mathematical model to allocate medical resources for the emergency department (ED). The optimal resource allocation is exploited by using some meta-heuristic algorithms, i.e., fast and elitism non-dominated sorting genetic algorithm (NSGA II), non-dominated sorting particle swarm algorithm (NSPSO), and non-dominated sorting differential evolution (NSDE). Thereafter, a dynamic simulation model, which embeds the solutions from the resource allocation model in the simulation process, is constructed. Each feasible solution from the three meta-heuristic algorithms is simulated to estimate the performance of the resources allocated in terms of the average service level and staff utilization. The results show that the performance of the NSGAII, where the average service level and staff utilization for the current resources are 0.844 and 0.751, respectively, is better than those of NSPSO and NSDE. Besides, the number of medical staff gives a significant effect on the service level and utilization while the number of beds only impacts staff utilization. The simulation model can find out that the best combination of the number of staff and the number of beds is from 1 to 10 staffs and 1–6 beds to maximize the utilization and service level. [ABSTRACT FROM AUTHOR]

Forecasting is important in the decision-making process. Therefore, among many forecasting algorithms that have been developed, fuzzy neural network (FNN) has been widely applied. The FNN structure is commonly trained using a back-propagation algorithm. However, this algorithm is sensitive to the initial weights and high computation, especially for complex problems. Thus, this study intended to overcome these drawbacks by applying the gradient evolution (GE) algorithm to train the intuitionistic FNN (IFNN). The proposed algorithm, GEIFNN, was verified using ten benchmark datasets. The results were compared with some other metaheuristic-based IFNN algorithms, such as genetic algorithm, particle swarm optimization algorithm, and differential evolution algorithm. The computational results show that the proposed GEIFNN relatively outperformed other tested algorithms, especially in training error. Furthermore, the proposed algorithm was applied to forecast medical costs for the treatment of acute hepatitis in Taiwan. The result also shows that GEIFNN can obtain the smallest training error. • Improve backpropagation on intuitionistic fuzzy neural network using a gradient evolution algorithm. • Verify the proposed algorithm with ten benchmark datasets. • Computational results show that the proposed algorithm gives promising results. • Apply the proposed algorithm to forecast medical cost for acute hepatitis treatment in Taiwan. • The contributions are developing GEIFNN algorithm and solving a real problem. [ABSTRACT FROM AUTHOR]