手法を比較

	GISベース多基準意思決定分析 (GIS-MCDA)	整数計画法	線形計画法
分野	空間分析	最適化	最適化
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	2006	1958	1947
提唱者	Jacek Malczewski (GIS-MCDA synthesis)	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)	George B. Dantzig
種類	Spatial multi-criteria suitability/decision analysis	Mathematical optimisation — exact combinatorial method	Mathematical programming / continuous optimization
原典	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	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136
別名	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	IP, MIP, mixed-integer programming, mixed-integer linear programming	LP, linear optimization, Doğrusal Programlama (LP)
関連	4	4	4
概要	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.	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.