Designing a humanitarian relief logistics network considering the cost of deprivation using a robust-fuzzy-probabilistic planning method

Abstract This study addresses deprivation costs in humanitarian logistics by incorporating direct and indirect impacts into a robust-fuzzy-probabilistic framework to optimize resource allocation and minimize delays in disaster response. This model has concepts such as facility location and optimal flow allocation. The results show that with the increase in the uncertainty rate and as a result of the increase in the number of people affected by the accident and the demand for relief items at the scene of the accident, the costs related to the supply and transportation of the items and the injured have increased. Also, by examining the effect of deprivation cost on the results of the model, it was observed that with the increase of deprivation cost, the model aims to increase the goods transferred to satisfy the demand fully. Therefore, the expenses related to deprivation have decreased, but on the other hand, the costs associated with the transfer of products have increased. As a result, the cost of the entire relief logistics network has increased. Finally, by examining the effects of the weighting coefficient of the objective function on the values of the objective function of the problem, it can be seen that with the increase of this weighting coefficient, the value of the objective function has increased due to the increase in the volume of transfers of relief items.