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TopIntroduction
Hazardous situations in human environments, with which we are not prepared to deal, may end in humanitarian crises (HC), ranging from drought, flooding, famine and hurricanes to wars, earthquakes and volcanic eruptions (Sphere Project, 2010). The correct information, which is either collected or created during HCs, is essential in managing humanitarian response (HR), saving lives and bringing such environments back to their normal state (UN, 2007). Many countries cannot deal with HC. They appeal to the international community to intervene and help in mitigating the catastrophe (HPG, 2006). There are various ways of addressing HC, but making a correct and prompt decision on who is going to intervene in HC, why, when and how they can help in HC, remains one of the most crucial and complex tasks in HR. Resource Allocations (RAlloc) often takes longer than expected (FEWSNET, 2001), (Charny, 2004), (HPG, 2005), (Margesson & Taft-Morales, 2010) and its efficacy depends on information availability and reliability (Morrison & Cohen, 2005), (Bharosa & Jansen, 2009). One of the most difficult tasks for decision makers in HR is to find appropriate humanitarian organizations to respond to HC. For every HC the list of requirements is different and needs in HC might not suit all humanitarian organization profiles.
The role of a coordinating body during HR is very important because it acts as a hub connecting:
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Actors, such as Donors (D), Agencies (A) and Implementing Partners (P)), which provide help;
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Sectors (S) which refer to the technical services provided by actors to affected populations, such as food, water, health, shelter, education; and
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Locations (L) which refer to the place where the affected population live.
In efficient RAlloc, the coordinating body uses its accumulated human experience to reason upon Agencies, Sectors and Locations, and performs RAlloc by selecting the best possible combination of Agencies and Sectors to work with each other in a particular Location. Given that RAlloc is a complex process, where decisions are taken under enormous pressure of time and resources, HR must be tailored to a particular HC and consider cultural, political and organizational practices and regulations. Having information systems, with well-structured repositories and the exchange of their contents in place, is one of the prerequisites for informed RAlloc (Shamoug & Juric, 2011; Shamoug & Juric, 2011A; Shamoug & Juric, 2012; Shamoug et al., 2012; Shamoug et al., 2012A; Shamoug et al.; Shamoug & Juric, 2017). Decision makers in RAlloc rely on such changes rather than waiting for accurate and exact information, which may come late, or not at all (Kari, 1998). RAlloc must be progressive, iterative, interpret what is going on rather than what should be done (Pollack-Johnson & Liberatore, 2006). It should collect data from people/machines, support data planning/tasking and provide data analysis (Kozlowski & Klien, 2000; Mackenzie, 2004; Lee & Cummins, 2004; Balci et al., 2011).
TopIn this paper we propose a software tool for Semantic Resource Allocation (SemRAlloc) with slightly different characteristics compared to other tools in decision making. The tool enables:
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Continuous support in RAlloc from day one of a HC and assistance in decision making, regardless of how much information is available;
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Collection and interpretation of the semantics of an environment where RAlloc is required;
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Reasoning upon semantics of that environment to make appropriate RAlloc.