Dynamical Supply Networks for Crisis and Disaster Relief: Networks Resilience and Decision Support in Uncertain Environments

Abstract

Recent natural disasters affected many parts of the world and resulted in an extensive loss of life and disruption of infrastructure. The randomness of impacts and the urgency of response efforts require a rapid decision making in an often uncertain and complex environment. In particular, the organization and controlling of efficient humanitarian supply chains are challenging the operational analyst from both the theoretical and practical perspective. A far-sighted and comprehensive emergency planning can alleviate the effects of sudden-onset disasters and facilitate the efficient delivery of required commodities and humanitarian aid to the victims. Methods from computational networks and agent-based modelling supported by sophisticated data farming experiments allow a detailed analysis of network performance measures and an evaluation of the vulnerability of infrastructure and supply networks. These approaches can be used for relief planning as well as for a simulation of continuous aid work threatened by severe disruptions. This paper presents a first step towards an integrated dynamic network optimization approach which combines forecasting models and simulation.