Optimal depot locations for humanitarian logistics service providers
When a disaster strikes an area, often international assistance is requested to help responding and recovering from the disaster. This response can be characterized by the dispatch of relief items to the affected regions. Relief agencies usually make use of storage space provided by Humanitarian Logistics Service Providers (HSLPs). One of the challenges an HLSP (which provides storage space) faces, is how to organize their network of depots. Important questions are how many depots do we need and what should be the locations? An optimal network aligns with two main objectives, the transportation costs and the maximum response time to a disaster. Based upon historical data, we determine depot locations, which are positioned in an optimal way for the historical disasters. We examine the trade-off between these two objectives using the Pareto front. Furthermore, we apply robust optimization to this problem in order to find solutions that are robust against uncertainty in the location and scale of future disasters. We apply the proposed procedure on a dataset from UNHRD and one from EM-DAT. We conclude that using robust optimization can reduce future costs significantly. However, incorporating enough data (at least 2 years of data) into the modeling will already lead to robust solutions using the nominal approach.