Optimization of simulated systems: OptQuest and alternatives

Kleijnen, J.P.C.; Wan, Jie

doi:10.1016/j.simpat.2006.11.001

Cited by 116 publications

(72 citation statements)

References 7 publications

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“…Like all practical simulation optimization methods, OptQuest is also an iterative heuristic. It treats the simulation model as a black box by observing only the input and the output of the simulation model (Kleijnen and Wan, 2007). It is an optimization tool that combines the meta-heuristics of tabu search, neural networks, and scatter search into a single search heuristic.…”

Section: Optquest Optimizationmentioning

confidence: 99%

Size Optimization of a Solar-wind Hybrid Energy System Using Two Simulation Based Optimization Techniques

Ekren¹,

Ekren²

2011

Fundamental and Advanced Topics in Wind Power

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Section: Optquest Optimizationmentioning

confidence: 99%

Size Optimization of a Solar-wind Hybrid Energy System Using Two Simulation Based Optimization Techniques

Ekren¹,

Ekren²

2011

Fundamental and Advanced Topics in Wind Power

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“…OptQuest works iteratively using a black-box approach as a general-purpose optimizer that performs a series of simulation experiments to nd optimal or near-optimal solutions. OptQuest utilizes a mix of meta-heuristics algorithms, including Scatter Search (SS), Genetic Algorithm (GA), Tabu Search (TS), and neural network learning algorithms, to nd the global optimum [42]. In the present study, OptQuest is employed which takes advantage of the decision-support features of the Enterprise Dynamics simulation software with the use of global optimization algorithms.…”

Section: Computational Results 71 Performancementioning

confidence: 99%

A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions

2017

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Abstract. Delays and disruptions reduce the reliability and stability of the rail operations.Railway tra c rescheduling includes ways to manage the operations during and after the occurrence of such disturbances. In this study, we consider the simultaneous presence of large disruptions (temporary full or partial blockage of tracks) as well as stochastic variation of operations as a source of disturbance. The occurrence time of blockage and its recovery time are given. We designed a simulation-based optimization model that incorporates dynamic dispatch priority rules with the objective of minimizing the total delay time of trains. We, moreover, designed a variable neighborhood search meta-heuristic scheme for handling tra c under the limited capacity close to the blockage. The new plan includes a set of new departure times, dwell times, and train running times. We evaluated the proposed model on a set of disruption scenarios covering a large part of the Iranian rail network. The result indicates that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly, when compared to commercial optimization software. In addition, the solutions have a lower average and smaller standard deviation than the currently accepted solutions, determined by human dispatcher or by standard software packages.

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“…Section 5.1 discusses the performance for a variant of the popular (s, S ) inventory problem by Bashyam and Fu (1998) (which was also used by Kleijnen and Wan 2007). Section 5.2 uses a call center staffing problem taken from Kelton et al (2007).…”

Section: Computational Resultsmentioning

confidence: 99%

Constrained Optimization in Simulation: A Novel Approach

2008

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This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespecified target values. Besides the simulation outputs, the simulation inputs must meet prespecified constraints including the constraint that the inputs be integer. The proposed heuristic combines (i) experimental design to specify the simulation input combinations, (ii) Kriging (also called spatial correlation modeling) to analyze the global simulation input/output data that result from this experimental design, and (iii) integer nonlinear programming to estimate the optimal solution from the Kriging metamodels. The heuristic is applied to an (s, S) inventory system and a realistic call-center simulation model, and compared with the popular commercial heuristic OptQuest embedded in the ARENA versions 11 and 12. These two applications show that the novel heuristic outperforms OptQuest in terms of search speed (it moves faster towards high-quality solutions) and consistency of the solution quality.

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Optimization of simulated systems: OptQuest and alternatives

Cited by 116 publications

References 7 publications

Size Optimization of a Solar-wind Hybrid Energy System Using Two Simulation Based Optimization Techniques

Size Optimization of a Solar-wind Hybrid Energy System Using Two Simulation Based Optimization Techniques

A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions

Constrained Optimization in Simulation: A Novel Approach

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