A decision support system for robust humanitarian facility location

Abstract

a b s t r a c tEach year, more than 400 natural disasters hit the world. To be more responsive, humanitarians organize stocks of relief items. It is an issue to know the quantity of items to be stored and where they should be positioned. Many authors have tried to address this issue both in industrial and humanitarian environments. However, humanitarian supply chains today do not perform correctly, particularly as regards resilience and efficiency. This is mainly due to the fact that when a disaster occurs, some hazards can strongly impact the network by destroying some resources or collapsing infrastructure. The expected performance of the relief response is consequently strongly decreased. The problem statement of our research work consists in proposing a decision-making support model in artificial intelligence dedicated to the humanitarian world and capable of designing a coherent network that is still able to adequately manage the response to a disaster despite failures or inadequacies of infrastructure and potential resources. This contribution is defined through a Stochastic Multi-Scenarios Program as a core and a set of extensions. A real-life application case based on the design of a humanitarian supply chain in Peru is developed in order to highlight the benefits and limits of the proposition.

Designing Valid Humanitarian Logistics Scenario Sets

“…Future uses of this approach and its results can be imagined. One concrete example of application is already developed in (Vargas et al, 2015) to design a robust network of disaster warehouses to respond to recurrent disasters in Peru. Table 6.…”

Natural outbreaks and bioterrorism: How to deal with the two sides of the same coin?

“…The preparation phase of the crisis can also benefit from AI tools by ordering the reuse of information from previous crises [ 83 ], improving the stockholders’ training with a serious game approach [ 84 ], helping to design realistic plans [ 85 ] or even boosting the discovery of new drugs [ 86 ]. Resources management can also be improved by the use of AI in terms of network structuration [ 87 ] as well as for the mean’s allocation [ 88 ]. During the crisis, AI can also sort information from many sensors, merge it and make it relevant for the user responsible of the situation assessment [ 89 ], which will be helped by a decision-support system [ 90 ] to design the best crisis response.…”

A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction

“…Such system uses newsvendors' variants that exploits demand uncertainty to treat an emerged event. A robust humanitarian decision support system for disaster recovery and planning is presented in [23]. Such a system exploits robust humanitarian facility location within the coverage area of municipality.…”