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| 다중 목표 큐잉 시뮬레이션× | 다목적 최적화× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s–2000s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| 창시자≠ | Operations research community (Banks, Deb, and related authors) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 유형≠ | Simulation-based multi-objective optimization | Optimization framework |
| 원전≠ | 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 |
| 별칭 | MOQS, Multi-criteria Queueing Simulation, Multi-objective Queue Optimization, Pareto Queueing Simulation | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. |
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