Linganisha mbinu

	Uigaji wa Matukio Yanayojitokeza kwa Malengo Mengi	Uboreshaji wa Malengo Mengi
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s–2000s	1896 (concept); 1989–2002 (evolutionary algorithms era)
Mwanzilishi	Various (DES: Tocher 1963; multi-objective integration: 1990s–2000s OR literature)	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Aina	Simulation-optimization hybrid	Optimization framework
Chanzo asilia	Kleijnen, J. P. C., & Gaury, E. (2003). Short-term robustness of production management systems: A case study. European Journal of Operational Research, 148(2), 452–465.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Majina mbadala	MO-DES, Multi-objective DES, Pareto-based discrete-event simulation, DES with multi-objective optimization	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Zinazohusiana	5	3
Muhtasari	Multi-Objective Discrete-Event Simulation (MO-DES) couples a discrete-event simulation engine with multi-objective optimization to explore trade-offs among two or more conflicting performance measures — such as throughput, cost, and waiting time — across stochastic, time-ordered process models. It is widely applied in manufacturing, logistics, healthcare, and service system design.	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.