방법 비교

	다목적 선형 계획법 (MOLP)	선형 계획법	다목적 최적화
분야	시뮬레이션	최적화	시뮬레이션
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1955–1986	1947	1896 (concept); 1989–2002 (evolutionary algorithms era)
창시자	Steuer, R. E.; Charnes, A.; Cooper, W. W.	George B. Dantzig	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
유형	Mathematical optimization / vector optimization	Mathematical programming / continuous optimization	Optimization framework
원전	Steuer, R. E. (1986). Multiple Criteria Optimization: Theory, Computation, and Application. John Wiley & Sons, New York. ISBN: 9780471888468	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
별칭	MOLP, Vector Linear Programming, Multi-criteria LP, Linear Vector Optimization	LP, linear optimization, Doğrusal Programlama (LP)	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
관련	3	4	3
요약	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.	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.