Multi-objective capacity allocation of hospital wards combining revenue and equity

Hospital wards are critical resources because of limited space and large construction investment and operating costs. They are shared among different types of patients with different access time targets determined by their diseases and payments. In countries like China, it is important for public hospitals to simultaneously maximize hospital revenue as well as equity among different types of patients when allocating these limited capacities. Consequently, hospital managers are under high pressure to consider different types of patients’ access time targets and allocate them wards without decreasing revenues. To address this problem, a multi-objective stochastic programming (MSP) model is proposed with the objective to maximize both revenue and equity. Random patient arrivals and lengths of stay make it difficult to analytically describe both revenue and equity objectives in the MSP model. To cope with this problem, a data-driven discrete-event simulation model is proposed to find the relationship between model objectives regarding system performance and decisions regarding capacity allocation and patient admission. Then, based on the simulation results, we propose a linearization approach to transform the complex multi-objective stochastic programming model to a multi-objective integer linear programming (MILP) model, and an adaptive improved ε-constraint algorithm and a multi-objective genetic algorithm combined with neighborhood search algorithm are proposed to solve the MILP problem. Based on the real data collected from a large public hospital in Shanghai, extensive numerical experiments are performed to demonstrate the efficiency of the model and solution approaches.