Сравнение методов

	Многокритериальная марковская модель	Многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2006	1896 (concept); 1989–2002 (evolutionary algorithms era)
Автор метода	Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.)	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Тип	Stochastic sequential decision model with multiple objectives	Optimization framework
Основополагающий источник	Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67–113.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Другие названия	MOMDP, Multi-objective MDP, Multi-criteria Markov Decision Process, MO-Markov Model	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Связанные	5	3
Сводка	A Multi-objective Markov Model (MOMDP) extends classical Markov Decision Processes to settings where an agent must optimize several reward signals simultaneously. Instead of a single optimal policy, the model produces a Pareto-optimal set of policies, enabling decision-makers to navigate trade-offs between competing goals such as cost, risk, and throughput over time.	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.