Linganisha mbinu

	Uprogramu Kamili wa Hesabu wa Kitakwimu	Uboreshaji wa Malengo Mengi ya Kistochastiki
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1955	1990s–2000s
Mwanzilishi	Dantzig, G. B.; Beale, E. M. L.	Various (Fonseca, Fleming, Deb, Zitzler, and others)
Aina	Optimization under uncertainty with discrete decisions	Stochastic metaheuristic optimization
Chanzo asilia	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Majina mbadala	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
Zinazohusiana	6	5
Muhtasari	Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved.	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.