Σύγκριση μεθόδων

	Προγραμματισμός Στόχων	Γραμμικός Προγραμματισμός	Βελτιστοποίηση Πολλαπλών Στόχων
Πεδίο	Λήψη Αποφάσεων	Βελτιστοποίηση	Προσομοίωση
Οικογένεια	MCDM	Process / pipeline	Process / pipeline
Έτος προέλευσης	1955	1947	1896 (concept); 1989–2002 (evolutionary algorithms era)
Δημιουργός	Charnes, A., Cooper, W. W.	George B. Dantzig	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Τύπος	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation	Mathematical programming / continuous optimization	Optimization framework
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	—	LP, linear optimization, Doğrusal Programlama (LP)	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Συναφείς	8	4	3
Σύνοψη	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.