Dynamic Relief Demand Management for Emergency Logistics Operations Under Large Scale Disasters
This paper presents a dynamic relief demand management model for emergency logistics operations under imperfect information conditions in large scale natural disasters. The proposed methodology consists of three steps: (1) data fusion to forecast relief demand in multiple areas, (2) fuzzy clustering to classify affected area into groups, and (3) multi criteria decision making to rank the order of priority of groups. The results of tests accounting for different experimental scenarios indicate that the overall forecast errors are lower than 10% inferring the proposed method's capability of dynamic relief demand forecasting and allocation with imperfect information to facilitate emergency logistics operations.