手法を比較

	多目的線形計画法（MOLP）	ゴールプログラミング	線形計画法	多目的最適化
分野	シミュレーション	意思決定	最適化	シミュレーション
系統	Process / pipeline	MCDM	Process / pipeline	Process / pipeline
提唱年	1955–1986	1955	1947	1896 (concept); 1989–2002 (evolutionary algorithms era)
提唱者	Steuer, R. E.; Charnes, A.; Cooper, W. W.	Charnes, A., Cooper, W. W.	George B. Dantzig	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
種類	Mathematical optimization / vector optimization	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation	Mathematical programming / continuous optimization	Optimization framework
原典	Steuer, R. E. (1986). Multiple Criteria Optimization: Theory, Computation, and Application. John Wiley & Sons, New York. ISBN: 9780471888468	Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
別名	MOLP, Vector Linear Programming, Multi-criteria LP, Linear Vector Optimization	—	LP, linear optimization, Doğrusal Programlama (LP)	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
関連	3	8	4	3
概要	Multi-Objective Linear Programming (MOLP) extends classical linear programming to handle several conflicting linear objective functions simultaneously over a feasible region defined by linear constraints. Instead of a single optimal solution, MOLP produces a Pareto-efficient frontier from which a decision-maker selects a preferred trade-off. It is foundational to operations research and management science for resource allocation, planning, and design problems with competing goals.	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.	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.	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.