A Multi-Objective Location-Allocation Model for Preventive Healthcare Systems with Probabilistic Demand

Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. The level of participation in preventive health care programs is a critical factor in terms of their effectiveness and efficiency of these programs. This article presents a methodology for locating preventive health care facilities (PHCFL) in order to increase the accessibility to potential clients and thus maximize participation in preventive healthcare programs. Due to capacity constraints in preventive health care facilities and the importance of waiting time in queues, we assume that each facility acts as M/M/ /  queuing system. We present an Integer nonlinear programming (INLP) model in order to maximize the participation rate and minimize the cost of building facilities and allocating medical equipments to open facilities. Because of the importance of other objective functions, supplements have been introduced to bring about equitable access to preventive health care centers and to balance the hours of servant unemployment.The model is known as NP-Hard models, hence we propose two meta-heuristics algorithms to solve this problem with multi-objective functions. Meta-heuristics algorithms are consists of: non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. In order to increase the speed and performance of the combinatorial optimization algorithms, new coding is used in these solution algorithms. Finally, we used Taguchi method to tune the parameters of two algorithms and test problems with different size were generated and analyzed. According to the results, non-dominated sorting genetic algorithm is better.