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	Modelli di Localizzazione-Assegnazione	Analisi Decisionale Multi-Criterio basata su GIS (GIS-MCDA)	Programmazione Intera
Campo	Analisi spaziale	Analisi spaziale	Ottimizzazione
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	1963	2006	1958
Ideatore	Leon Cooper; S. L. Hakimi	Jacek Malczewski (GIS-MCDA synthesis)	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)
Tipo	Spatial facility-location optimization	Spatial multi-criteria suitability/decision analysis	Mathematical optimisation — exact combinatorial method
Fonte seminale	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669
Alias	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	IP, MIP, mixed-integer programming, mixed-integer linear programming
Correlati	4	4	4
Sintesi	Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.	GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data.	Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.