Сравнение методов

	Смешанное целочисленное программирование с множеством целевых функций	Многокритериальная оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1980s–2000s	1896 (concept); 1989–2002 (evolutionary algorithms era)
Автор метода	Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Тип	Mathematical optimization	Optimization framework
Основополагающий источник	Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin. ISBN: 9783540213987	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Другие названия	MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Связанные	5	3
Сводка	Multi-Objective Mixed-Integer Programming (MO-MIP) is an optimization framework that simultaneously optimizes two or more conflicting objective functions subject to linear or nonlinear constraints, where some decision variables are restricted to integer values and others are continuous. It is widely applied in engineering design, supply chain planning, resource allocation, and scheduling problems that require discrete choices alongside continuous quantities.	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.