Sammenlign metoder

	Stokastisk multi-objektiv optimering	Stochastic Dynamic Programming
Fagfelt	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1990s–2000s	1957
Opphavsperson	Various (Fonseca, Fleming, Deb, Zitzler, and others)	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
Type	Stochastic metaheuristic optimization	Sequential optimization under uncertainty
Opprinnelig kilde	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
Alias	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization	SDP, Markov Decision Process, MDP, Stochastic DP
Relaterte	5	6
Sammendrag	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.	Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.