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。
| 多目标目标规划× | 多目标优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1961 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| 提出者≠ | Charnes, A. and Cooper, W. W. | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 类型≠ | Mathematical programming / multi-criteria optimization | Optimization framework |
| 开创性文献≠ | Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 978-0471148258 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 别名 | MOGP, Multi-goal programming, Vector goal programming, Multi-criteria goal programming | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 相关≠ | 4 | 3 |
| 摘要≠ | Multi-Objective Goal Programming (MOGP) is a mathematical programming technique that simultaneously pursues several aspirational targets by minimizing weighted deviations from each goal. Rooted in Charnes and Cooper's original goal programming framework (1961), MOGP extends it to handle multiple competing objectives, making it indispensable in operations research, supply chain design, resource allocation, and policy analysis where decision-makers must satisfy — or come close to — multiple conflicting requirements at once. | 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 ↗ |
|
|