Salīdzināt metodes

	Location-Allocation	Multi-kritēriju lēmumu analīze, kas balstīta uz ĢIS (GIS-MCDA)	Lineārā programmēšana
Nozare	Telpiskā analīze	Telpiskā analīze	Optimizācija
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	1963	2006	1947
Autors	Leon Cooper; S. L. Hakimi	Jacek Malczewski (GIS-MCDA synthesis)	George B. Dantzig
Tips	Spatial facility-location optimization	Spatial multi-criteria suitability/decision analysis	Mathematical programming / continuous optimization
Pirmavots	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.	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136
Citi nosaukumi	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	LP, linear optimization, Doğrusal Programlama (LP)
Saistītās	4	4	4
Kopsavilkums	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.	Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences.