fuzzy programming

In today's dynamic and unpredictable world, the planning and management of humanitarian supply chains hold paramount importance. Efficient logistics management is crucial for effectively delivering essential aid and resources to affected areas during disasters and emergencies, ensuring timely support and relief to vulnerable populations. In this research, we addressed a novel humanitarian supply chain network design problem that considers product differentiation and demand uncertainty. Specifically, we simultaneously incorporate non-perishable, perishable, and blood products as critical components of the network. The problem is formulated as a multi-objective mixed-integer linear programming model aiming to minimize the total cost and total traveled distance of products by making location, allocation, and production decisions. To enhance realism, we account for demand uncertainty in affected areas. To tackle this challenging problem, we proposed a two-phase solution methodology. Firstly, we employed a robust optimization approach to establish a deterministic counterpart for the stochastic model. Subsequently, an efficient fuzzy programming-based approach reformulates the model into a single-objective form, effectively accommodating decision-makers' preferences. Numerical instances are solved to investigate the performance of the model and solution methodologies. The results demonstrate the effectiveness of our fuzzy approach in finding non-dominated solutions, enabling decision-makers to explore trade-offs. Also, sensitivity analyses were conducted to provide more insights. Finally, some suggestions are presented to extend the current work by feature researchers.