Dynamic inventory decisions for humanitarian aid materials considering budget limitations

In case of disaster, providing relief supplies to the affected people has vital importance. Governmental or nongovernmental organizations (NGOs) prepare for disasters by purchasing and stockpiling these aid materials in appropriate quantities. They operate under a limited budget and this budget can either be used before the disaster for stocking decisions under uncertainty or it can be used after the disaster to satisfy the required demand at a higher cost when the uncertainty is resolved. If all the budget is used before the disaster, and if there is no disaster in a long term, there will be a high holding cost. On the other hand, if all the budget is reserved for use after the disaster, meeting the demand will be more costly or the demand may not be met within a certain period of time. Thus, NGOs need to decide how to allocate the budget for pre and post-disaster usage. In this system, the budget of NGOs may also change over time through donations or other incomes. In this point of view, NGOs need to make dynamic stock and budget allocation decisions, under the available budget at hand. In our study, we analyze the dynamic stocking decisions of NGOs using stochastic dynamic programming formulations under budget constraints. We develop infinite horizon stochastic dynamic programming models with and without budget considerations, and compare the results of these models via numerical analysis. Detailed numerical studies and results of the sensitivity analysis show the significance of budget considerations in inventory decisions and the effects of different parameters on the system results.

Automated summary

In case of disaster, NGOs need to make decisions on how to allocate budget for pre and post-disaster usage. This is because using all the budget before the disaster can lead to high holding costs if there is no disaster in the long term, while reserving all the budget for use after the disaster may result in higher costs or unmet demand. Our study analyzes the dynamic stocking decisions of NGOs under budget constraints using stochastic dynamic programming models.