Optimal depot locations for humanitarian logistics service providers using robust optimization

When a disaster strikes an area, often international assistance is requested to help responding and recovering from the disaster. The response characterized by dispatch of relief items affected regions via depots Humanitarian Logistics Service Providers (HLSPs). One challenges HLSP faces how organize its network depots. Important questions are many should be opened what their locations be? We develop method that uses historical data determine depot minimize cost transportation maximum time examine trade-off between two objectives using Pareto front. Furthermore, we use robust optimization find solutions against uncertainty in location scale future disasters. provide case study United Nations Response Depot (UNHRD), large globally operating HLSP. Among other things, compute potential savings could obtained when expanding with additional Also, show need for looking generalize approach applying it independent database EM-DAT, which focus on examining added value framework. first result holds all Uncapacitated Facility Location Problems (UFLP) uncertain demand, used optimal number conclude incorporating likely advantage larger networks having less expected transportation, enhances decision-making regarding Even if at some point increasing allowed may not decrease significantly, reduce this transportation. For second result, consider UFLP finding given based nominal perform similar individual years included optimization, but guarantee smaller worst scenarios. This reduction especially valuable solution has extreme