Multi-Objective Evolutionary Algorithms for a Healthcare Facility Network Design

Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by means of protection and early detection. in this paper, a bi-objective mathematical model is proposed to design a network of preventive healthcare facilities in which each facility acts as m/m/l queuing system so as to minimize total travel and waiting time as well as establishment and staffing cost. the number of facilities to be established, the location of each facility, and the level of technology for each prospect facility are provided as the main determinants of a healthcare facility network. since the developed model of the problem is of an np-hard type, tri- meta-heuristic algorithms are proposed to solve the problem. initially, pareto-based meta-heuristic algorithm, which is called multi-objective simulated annealing (mosa), is proposed to solve the problem. subsequently, obtained results are validated by means of two popular algorithms, namely non-dominated sorting genetic algorithm (nsga-ii) and non-dominated ranking genetic algorithm (nrga). considering that solution-quality of all meta-heuristic algorithms heavily depends on their parameters, taguchi method is used to fine tune parameters of the employed algorithms. the computational results, obtained by implementing the algorithms on several problems of different sizes, demonstrate the reliability of the proposed methodology. it efficiently minimizes establishment and staffing costs, as well as travel and waiting time for the service, something which is directly related to the ultimate goal of managerial strategies for maximum preventive healthcare participation achievement.