A resilient model for humanitarian relief logistics: integrating relief time, health services, and hygiene items for sustainable development goals

Disasters impose huge human and financial losses and hinder humanitarian logistics. This study aims to find an efficient solution for humanitarian relief logistics in disasters by reducing time and cost. A sustainable-resilient multi-commodity, multi-depot, multi-objective, mixed-integer programming model is proposed for humanitarian relief logistics networks. The model considers network disruption, backup facilities, and rescue operations within standard relief time and golden time. The model utilizes a heterogeneous fleet of vehicles at different speeds, considering physical and mental health services at mild, moderate, and severe levels, and hygiene items such as diapers and sanitary pads in shelters. These components are included in the model to achieve Sustainable Development Goals and sustainability, the most important of which is Good Health and Well-being. After linearization, a case study is conducted to assess the validity of the model. Our proposed model is designed for a two-echelon location-allocation problem integrating pre- and post-disaster actions and solved via the augmented ε-constraint method in CPLEX/GAMS. It is observed that the model effectively allocates demands within a reasonable time and cost, finds facility locations, and activates backup facilities when warehouses are damaged due to disruption. The obtained results of the multi-objective model illustrate a trade-off between the objective functions, where a slight decrease in the response time leads to significantly higher costs. The sensitivity analysis demonstrates that an increase in demand leads to higher variable costs and longer response time but does not affect fixed costs. Furthermore, increasing the number of facility disruptions extends the relief time.