A survey of recent advances in discrete input parameter discrete-event simulation optimization

Swisher, James R.; Hyden, Paul; Jacobson, Sheldon H.; Schruben, Lee W.

doi:10.1080/07408170490438726

Cited by 59 publications

(22 citation statements)

References 105 publications

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“…The success of a particular stochastic approximation algorithm depends heavily on the defined neighborhood structure. Stochastic approximation algorithms can generally be shown to be probably convergent [40][41][42].…”

Section: Stochastic Approximation Methodsmentioning

confidence: 99%

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Zheng

Tan

Zhang

et al. 2010

Neural Comput & Applic

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Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization.

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Section: Stochastic Approximation Methodsmentioning

confidence: 99%

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Zheng

Tan

Zhang

et al. 2010

Neural Comput & Applic

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“…Another approach for combining forward and adjoint estimates is described [13] where the Fraser-Potter formula [19] is used to construct a smoothed estimate. Fixed-interval smoothers that differ from the formulations within [2], [4], [6]- [8], [10], [13] There are three kinds of smoothers: fixed point, fixed lag, and fixed interval (see [8]). This note is concerned exclusively with continuoustime H 1 fixed-interval smoothing.…”

Section: A Solution To the Continuous-time Fixed-interval Smoother Prmentioning

confidence: 99%

“…Recently, discrete optimization via simulation (DOvS) techniques have been developed to solve stochastic optimization problems for which the optimality is measured based on simulation results and the decision variables are discrete (see [3], [13], and [14] for surveys). Discrete optimization problems with small size solutions space can be efficiently solved using ranking and selection methods [4].…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Discrete Optimization of Stochastic Discrete Event Systems Subject to Non Closed-Form Constraints

Sava

Xie

2009

IEEE Trans. Automat. Contr.

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This technical note addresses the discrete optimization of stochastic discrete event systems for which both the performance function and the constraint function are not known but can be evaluated by simulation and the solution space is either finite or unbounded. Our method is based on random search in a neighborhood structure called the most promising area proposed in [7] and a moving observation area. The simulation budget is allocated dynamically to promising solutions. Simulation-based constraints are taken into account in an augmented performance function via an increasing penalty factor. We prove that under some assumptions, the algorithm converges with probability 1 to a set of true local optimal solutions. These assumptions are restrictive and difficult to verify but we hope that the encouraging numerical results would motivate future research exploiting ideas of this technical note.

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“…The combination of simulation, ANN, and optimization technique provides an e ective means for the high complex optimization problems. For more information on simulationoptimization approaches, advances, and applications, we refer readers to recent reviews by Swisher et al [23] and Fu et al [24].…”

Section: Proposed Ann-based Optimizationmentioning

confidence: 99%

An ANN-based optimization model for facility layout problem using simulation Technique

Azimi

Soofi

2017

Scientia Iranica

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Abstract. A real manufacturing system faces lots of real-world situations, such as stochastic behaviors; the lack of attention to this issue is noticeable in the previous research. The aim of this paper is to nd the optimum layout and the most appropriate handling transporters for the problem by a novel solving algorithm. The new model contains two objective functions including the Material Handling Costs (MHC) and the complication time of jobs (makespan). Real-world situations such as stochastic processing times, random breakdowns, and cross tra cs among transporters are considered in this paper. Several experiment designs have been produced using DOE technique in simulation software and an Arti cial Neural Network (ANN) as a meta-model is used to estimate the objective functions in the metaheuristic algorithms. A hybrid non-dominated sorting genetic algorithm (H-NSGA-II) is applied for the optimization task. The proposed methodology is evaluated through a real case study. First, simulation model is validated by comparing it with a real data set. Then, the prediction performance of ANN is investigated. Finally, the ability of H-NSGA-II in searching the solution space is compared with the traditional NSGA-II. The results show that the proposed approach, combing simulation, ANN, and H-NSGA-II, provides promising solutions for practical applications.

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A survey of recent advances in discrete input parameter discrete-event simulation optimization

Cited by 59 publications

References 105 publications

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Simulation-Based Discrete Optimization of Stochastic Discrete Event Systems Subject to Non Closed-Form Constraints

An ANN-based optimization model for facility layout problem using simulation Technique

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