השוואת שיטות

	מודלים של הקצאת מיקום	תכנון בשלמים	ניתוח נתיב בעלות מינימלית / ניתוח מרחק-עלות
תחום	ניתוח מרחבי	אופטימיזציה	ניתוח מרחבי
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	1963	1958	1994
הוגה השיטה	Leon Cooper; S. L. Hakimi	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)	Edsger Dijkstra (shortest path); GIS cost-surface adaptation
סוג	Spatial facility-location optimization	Mathematical optimisation — exact combinatorial method	Raster cost-surface routing
מקור מכונן	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.	Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669	Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.
כינויים	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	IP, MIP, mixed-integer programming, mixed-integer linear programming	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol
קשורות	4	4	3
תקציר	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.	Least-cost path analysis finds the route between two locations that minimizes accumulated travel cost across a landscape, rather than minimizing straight-line distance. By encoding terrain, slope, land cover, and other frictions into a cost surface and accumulating cost outward from a source, it identifies optimal corridors for roads, pipelines, trails, power lines, and wildlife movement — a core raster-GIS technique built on Dijkstra's shortest-path logic.