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| 确定性多目标优化× | 多目标线性规划 (MOLP)× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1951–1999 | 1955–1986 |
| 提出者≠ | Kuhn, H. W., Tucker, A. W. (Pareto optimality formalized); Miettinen, K. (systematic deterministic framework) | Steuer, R. E.; Charnes, A.; Cooper, W. W. |
| 类型≠ | Optimization framework — deterministic Pareto and scalarization methods | Mathematical optimization / vector optimization |
| 开创性文献≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 978-0-471-87339-6 | Steuer, R. E. (1986). Multiple Criteria Optimization: Theory, Computation, and Application. John Wiley & Sons, New York. ISBN: 9780471888468 |
| 别名 | Deterministic MOO, Classical Multi-Objective Optimization, Non-Stochastic MOO, Deterministic Pareto Optimization | MOLP, Vector Linear Programming, Multi-criteria LP, Linear Vector Optimization |
| 相关 | 3 | 3 |
| 摘要≠ | Deterministic Multi-Objective Optimization (Deterministic MOO) is a family of classical optimization approaches that simultaneously minimize or maximize multiple conflicting objective functions over a deterministic feasible set. It produces a Pareto front — the set of non-dominated solutions — from which a decision-maker selects the preferred trade-off. Unlike stochastic variants, all objective evaluations and constraints are fixed and noise-free. | 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. |
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