After disasters occurred, many refugees have to suffer a lot. To relieve this detrimental situation, various commodities are distributed to the pre-determined warehouses. However, the initial multi-commodity distribution may be imperfect, which results in some warehouses having surplus commodities compared to other unmet warehouses. Hence, it is necessary to rebalance commodities among those warehouses. Because of the uncertain environment after a disaster, the demand is usually uncertain. To plan this multi-commodity rebalancing process appropriately, it is usually assumed that the collected data are uncontaminated. However, this assumption can be easily violated due to the uncertain environment or human error, which results in the biased estimation of the solution. In this study, we propose a strategy for remedying the difficulties associated with data contamination so that a set of robust decisions are obtained. Through a case study, we show that the proposed strategy facilitates effective decision-making in the multi-commodity rebalancing when the data contamination is involved.