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	Modele lokalizacyjno-alokacyjne	Programowanie całkowitoliczbowe	Programowanie liniowe
Dziedzina	Analiza przestrzenna	Optymalizacja	Optymalizacja
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	1963	1958	1947
Twórca	Leon Cooper; S. L. Hakimi	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)	George B. Dantzig
Typ	Spatial facility-location optimization	Mathematical optimisation — exact combinatorial method	Mathematical programming / continuous optimization
Źródło pierwotne	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.	Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136
Inne nazwy	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	IP, MIP, mixed-integer programming, mixed-integer linear programming	LP, linear optimization, Doğrusal Programlama (LP)
Pokrewne	4	4	4
Podsumowanie	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.	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.	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.