Keywords: decision-making, network optimisation, clustering, graph theory, systems thinking, hierarchical decomposition, resource allocation, global catastrophes, infrastructure networks, complex networks, disastrous events, disasters, assistance needs, available resources, disaster management, emergency management, emergency recovery, computational complexity, hierarchical structures, clustering algorithms, reduced solution spaces, computational improvement, topological information, support centres, emergency events, network topology, risk assessment, risk management, catastrophic risks
Optimisation-based decision-making for complex networks in disastrous events
Assistance needs after large catastrophes often exceed available resources. Effective resource allocation is paramount to support emergency management and recovery, particularly within infrastructure networks. However, network optimisation problems exhibit high computational complexity, becoming intractable at a global scale. This paper successfully handles complexity through a systems approach, which uses a description of networks at different levels of abstraction through a hierarchical structure. The community structure of networks is unravelled via clustering algorithms that successively partition them hierarchically. A resource allocation problem is formulated adding information from the hierarchy, leading to a reduced solution space. Besides computational improvement, decisions are enhanced due to the topological information provided by the hierarchy-based optimisation. An example regarding the allocation of support centres aims to maximise assistance, at minimum cost, in case of emergency events. Solutions that respond to the network topology are obtained in a fraction of the time required by standard formulations.