Solving a new bi-objective model for relief logistics in a humanitarian supply chain using bi-objective meta-heuristic algorithms

One of the most important factors in a humanitarian supply chain during a disaster is a timely and ecient response. Delivering emergency commodities to the aected areas is also of signicance in reducing consequences. Moreover, transferring the injured people in the fastest and shortest time period using all available resources is quite important. To this end, a multi-echelon multi-objective forward and backward relief network is proposed that considers the location of hospitals, local warehouses, and hybrid centers which are hospital-warehouse centers in the pre-disaster phase. In the postdisaster phase, routing the relief commodities should be considered in the forward route. In the backward route, some vehicles that can transfer the injured people after delivering commodities, called hybrid transportation facilities, will take the injured to hospitals and hybrid centers. According to the degree of hardness, a hybrid Non-dominated Sorting Genetic Algorithm (NSGA-II) with Simulated Annealing (SA) and Variable Neighborhood Search (VNS) algorithms was proposed to solve the given problems. The results obtained from this hybrid algorithm were compared with those from NSGA-II and multi-objective SA-VNS using ve metrics (i.e., the number of Pareto, mean ideal distance, spacing, diversity, and time), and it was concluded that the proposed hybrid algorithm outperformed the two foregoing algorithms.