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An iterative surrogate-based optimization approach for multi-server queuing system design
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AI
DOI：10.1016/j.simpat.2025.103119.png)
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Abstract
En 中文
Queuing systems play an important role in numerous domains, including banks, supermarkets, traffic control, call centers, and production processes. Traditional methods for designing multi server queuing systems often rely on trial-and-error or extensive simulations, making them time-consuming and computationally expensive. This paper addresses these challenges using MEVO (Metamodel-based Evolutionary Optimizer), a surrogate-based optimization algorithm. MEVO employs a machine-learning model as a surrogate model, reducing reliance on computationally intensive simulations. The algorithm also integrates evolutionary operators for efficient solution space exploration, a long-term memory strategy to avoid redundant simulations, a dynamic search space reduction mechanism to enhance optimization efficiency. A case study of a supermarket checkout system, modeled in FlexSim, demonstrates algorithm's efficacy in optimizing queuing configurations under stochastic variables such customer arrival rates, basket sizes, and transaction values. MEVO achieves solution-quality performance comparable to the FlexSim optimizer while significantly reducing computation times. MEVO also delivers comparable computational performance to Bayesian optimization while exhibiting lower variance in objective-function results than FlexSim, highlighting consistency and robustness.
Keywords:
Queuing systems
Simulation-based optimization
Discrete event simulation
Surrogate-based optimization
Iterative surrogate-based optimization
Stochastic variables
Surrogate modeling
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2.6K
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