Linganisha mbinu

	Uboreshaji wa Malengo Mengi ya Kistochastiki	Uiguzi wa Monte Carlo
Nyanja	Uigaji	Ufanyaji Maamuzi
Familia	Process / pipeline	MCDM
Mwaka wa asili	1990s–2000s	1949
Mwanzilishi	Various (Fonseca, Fleming, Deb, Zitzler, and others)	Metropolis, N., Ulam, S.
Aina	Stochastic metaheuristic optimization	Robustness wrapper — Monte Carlo uncertainty propagation
Chanzo asilia	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Majina mbadala	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization	—
Zinazohusiana	5	0
Muhtasari	Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.