Applying Heuristic Algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff

Abstract

To address the inter-hospital hierarchical allocation and scheduling problems, this research used the pooling resource concept to allocate medical staff among hospital branches as well as determine their monthly schedules. This study proposed a two-stage strategy. The first stage proposed three heuristic algorithms—HRA1 (human resource allocation based on the hospital's size), HRA2 (human resource allocation based on the average allocation), and HRA3 (human resource allocation based on the severity of penalties)—for medical staff allocation. The second stage used the improved particle swarm optimization (PSO) algorithm to schedule the medical staff within a reasonable time. Based on the numerical results, HRA3 was superior to HRA1 and HRA2. Furthermore, the analysis of two scenarios—varying the sizes of hospital branches (Scenario 1) and varying the total number of medical staff (Scenario 2)—showed that, when the sizes of hospital branches varied (Scenario 1), HRA3 was superior to HRA1 and HRA2 whereas, when the sizes were given (Scenario 2), the lowest number of medical staff possible was approximately 60. The findings of this research will help hospital managers make decisions concerning the allocation and scheduling of medical staff.

Keywords

• Heuristic algorithms
• Staff allocation
• Staff scheduling
• Inter-hospital
• Particle swarm optimization