विधियों की तुलना करें

	बहु-उद्देश्यीय रैखिक प्रोग्रामिंग (MOLP)	लक्ष्य प्रोग्रामिंग	बहु-उद्देश्यीय अनुकूलन
क्षेत्र	अनुकरण	निर्णयन	अनुकरण
परिवार	Process / pipeline	MCDM	Process / pipeline
उद्भव वर्ष	1955–1986	1955	1896 (concept); 1989–2002 (evolutionary algorithms era)
प्रवर्तक	Steuer, R. E.; Charnes, A.; Cooper, W. W.	Charnes, A., Cooper, W. W.	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
प्रकार	Mathematical optimization / vector optimization	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation	Optimization framework
मौलिक स्रोत	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	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
उपनाम	MOLP, Vector Linear Programming, Multi-criteria LP, Linear Vector Optimization	—	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
संबंधित	3	8	3
सारांश	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.	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.