方法对比
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| 多目标情景分析× | 多目标优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2013 (integrated framework); scenario analysis roots: 1967 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| 提出者≠ | Stewart, French & Rios (integration formalized); scenario analysis roots: Kahn & Wiener (1967) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 类型≠ | Structured qualitative-quantitative hybrid | Optimization framework |
| 开创性文献≠ | Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4), 679-688. DOI ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 别名 | MOSA, Multi-criteria scenario analysis, Multi-objective futures analysis, MO-scenario analysis | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 相关≠ | 4 | 3 |
| 摘要≠ | Multi-objective Scenario Analysis (MOSA) is a structured method that constructs a set of plausible future scenarios and evaluates each scenario against multiple competing objectives or criteria. By making trade-offs explicit across objectives and across possible futures, it supports strategic decisions where uncertainty about the future and conflicts between goals co-exist. It is widely applied in energy planning, climate adaptation, public policy, and corporate strategy. | 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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