Bandingkan metode

	Simulasi Antrean Multi-Objektif	Simulasi Kejadian Diskrit Multi-Objektif
Bidang	Simulasi	Simulasi
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1990s–2000s	1990s–2000s
Pencetus	Operations research community (Banks, Deb, and related authors)	Various (DES: Tocher 1963; multi-objective integration: 1990s–2000s OR literature)
Tipe	Simulation-based multi-objective optimization	Simulation-optimization hybrid
Sumber perintis	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Pearson Prentice Hall. ISBN: 9780136062127	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.
Alias	MOQS, Multi-criteria Queueing Simulation, Multi-objective Queue Optimization, Pareto Queueing Simulation	MO-DES, Multi-objective DES, Pareto-based discrete-event simulation, DES with multi-objective optimization
Terkait	4	5
Ringkasan	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 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.