方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 目标规划× | 线性规划× | 多目标优化× | |
|---|---|---|---|
| 领域≠ | 决策 | 优化 | 仿真 |
| 方法族≠ | MCDM | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1955 | 1947 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| 提出者≠ | Charnes, A., Cooper, W. W. | George B. Dantzig | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 类型≠ | Multi-objective optimisation — weighted/lexicographic goal deviation minimisation | Mathematical programming / continuous optimization | Optimization framework |
| 开创性文献≠ | 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 |
| 别名≠ | — | LP, linear optimization, Doğrusal Programlama (LP) | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 相关≠ | 8 | 4 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|
|