The Hospital Location-Hardening Problem with Constrained Sources and Predetermined Capacities in Presence of Disruption and Facilities Failure Conditions

Today, the simultaneous reduction of location costs and transportation costs in the establishment of urban facilities is of critical importance. This becomes extremely sensitive when the area under consideration is in crisis situations and the inevitable transit of vehicles and inland transportation is difficult in those circumstances and may cause financial and irreparable damages.
In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points. Regarding to the bi-objective mathematical model, the ε-constraint and GAMS software are applied. According to the application of the proposed model for the large-scale problems and The NP-Hard structure of the problem, the NSGA-II is applied and evaluated.  In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points.
According to the two-objective mathematical model, the ε-constraint method and the GAMS 24.1.2 software are used. Due to the possibility of using the model in the large dimensional applications as well as the NP-hardness of the model, the NSGA-II meta-algorithm is applied and the efficiency of the proposed approach is examined and evaluated.