Humanitarian logistics planning for natural disaster response with Bayesian information updates

The current study proposes a sequential approach for humanitarian logistics in natural disasters based on the Bayesian group information updates (GIU). First, a dynamic time-dependent nonlinear model without GIU is proposed. Then, two losses are addressed to explain the influence of a disaster on supply, demand, and humanitarian logistics. The two losses include losses caused by the mismatch between supply and demand in affected areas and the time losses caused by logistics processes under emergency conditions. Therefore, a multi-period humanitarian logistics planning model with GIU is established based on the model without GIU using Bayesian theory. Then, the model with GIU is revised into a single-objective model, and then a matrix-coding-based genetic algorithm is developed to solve the revised model. Finally, the proposed methodology is applied to the humanitarian logistics problems of emergency response encountered during the Wenchuan Earthquake in China. Computational results show that the proposed methodology can generate specific logistics plans for allocating relief resources according to updated information. Therefore, emergency planners can gain insights for humanitarian logistics planning in natural disaster response by inputting their own sets of data.