手法を比較

	多目的最適化	ゴールプログラミング	混合整数計画法
分野	シミュレーション	意思決定	シミュレーション
系統	Process / pipeline	MCDM	Process / pipeline
提唱年	1896 (concept); 1989–2002 (evolutionary algorithms era)	1955	1958–1960
提唱者	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	Charnes, A., Cooper, W. W.	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
種類	Optimization framework	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation	Mathematical optimization
原典	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
別名	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	—	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
関連	3	8	6
概要	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.	GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.