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| Pemrograman Tujuan× | Pemrograman Linear× | Optimasi Multi-Objektif× | |
|---|---|---|---|
| Bidang≠ | Pengambilan Keputusan | Optimasi | Simulasi |
| Keluarga≠ | MCDM | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1955 | 1947 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Pencetus≠ | Charnes, A., Cooper, W. W. | George B. Dantzig | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Tipe≠ | Multi-objective optimisation — weighted/lexicographic goal deviation minimisation | Mathematical programming / continuous optimization | Optimization framework |
| Sumber perintis≠ | Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗ | 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 |
| Alias≠ | — | LP, linear optimization, Doğrusal Programlama (LP) | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Terkait≠ | 8 | 4 | 3 |
| Ringkasan≠ | 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. |
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