Confronta i metodi

	Simulazione di code multi-obiettivo	Ottimizzazione Multi-Obiettivo
Campo	Simulazione	Simulazione
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1990s–2000s	1896 (concept); 1989–2002 (evolutionary algorithms era)
Ideatore	Operations research community (Banks, Deb, and related authors)	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Tipo	Simulation-based multi-objective optimization	Optimization framework
Fonte seminale	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Pearson Prentice Hall. ISBN: 9780136062127	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Alias	MOQS, Multi-criteria Queueing Simulation, Multi-objective Queue Optimization, Pareto Queueing Simulation	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Correlati	4	3
Sintesi	Multi-objective queueing simulation combines discrete-event queueing models with multi-objective optimization to simultaneously evaluate and optimize conflicting performance metrics — such as average wait time, server utilization, throughput, and service cost — across a simulated queuing system. It produces a Pareto front of non-dominated solutions rather than a single optimal point, enabling decision-makers to understand trade-offs explicitly.	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.