A Unifying View of Hybrid Simulation/Analytic Models and Modeling

Shanthikumar, J. George; Sargent, Robert G.

doi:10.1287/opre.31.6.1030

Cited by 184 publications

(95 citation statements)

References 29 publications

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“…Nevertheless, combining queueing networks with simulation can provide an efficient means to validate complex models by vol. 27, n° 2, 1993 testing special cases whose performance are, in the long run, predictable (Shanthikumar and Sargent, 1983;. 4) They rarely respond to any unexpected situation in real time when the environment changes.…”

Section: Resultsmentioning

confidence: 99%

Network of queues modeling in flexible manufacturing systems : a survey

Hsu¹,

Tapiero²,

Lin³

1993

RAIRO-Oper. Res.

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Section: Resultsmentioning

confidence: 99%

Network of queues modeling in flexible manufacturing systems : a survey

Hsu¹,

Tapiero²,

Lin³

1993

RAIRO-Oper. Res.

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“…Hybrid modeling (see Shanthikumar and Sargent [1983] and Sargent [1994]) involves the combination of analytic and simulation models to solve a problem. In this section, we present solution approaches that will be investigated within this research related to hybrid analysis of logistical supply chains.…”

Section: Solution Methodsmentioning

confidence: 99%

Hybrid Simulation/Analytic Models for Military Supply Chain Performance Analysis

Farris¹,

Rossetti²

2004

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYYJ SUPPLEMENTARY NOTES ABSTRACTThis research is intended to extend the knowledge base concerning logistical network modeling and design. Basic research techniques were developed to begin to model logistical networks within a hybrid simulation/analytic framework. The first step in this process is to develop robust approximations for portions of large-scale simulation models. This research examined the novel idea of utilizing neural networks as a meta-modeling technique to replace specific aspects of a simulation. This work started with the replacement of queueing stations within a logistics network. Any logistics network can be formulated as a network of material flowing through processes requiring resources. A new methodology was developed for forming approximations and improved approximators for queueing stations within a logistics network. The motivation for developing this approximation is the integration of such approximation in hybrid simulation/analytic methods for evaluating logistic networks. Future work should investigate the performance of these approximations within the larger logistical network context. We demonstrate the use of neural networks to close the gap between the output of Whitt's GI/G/m approximation and the results obtained via simulations of a GI/G/m queue. Once the neural network has been trained, we can feed in the parameters and resulting output of Whitt's GI/G/m approximation, along with additional information describing the arrival and service distributions of the queue, and yield an acceptably accurate approximation for the expected wait time in a GIIG/m queue. These approximations can be embedded in parametric decomposition algorithms for the logistics network as a whole. The motivation for developing this approximation is the integration of such approximation in hybrid simulation/analytic methods for evaluating logistic networks. The neural network approximation developed easily beats Whitt's approximation for correlated arrivals, but further investigation is needed to assure a robust approximation. Future work should investigate the performance of these app...

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“…MMH, SMF). Likewise, AME and SG methods could be divided according to the extent of the analytical and simulation models (which may result in a hybrid model or hybrid modelling -see [10]). That is the essential difference between SPDE and ROSA, for instance.…”

Section: Interaction Between Simulation and Optimizationmentioning

confidence: 99%

“…the solution space and objective function [6,7,8,9]. Shanthikumar and Sargent [10] suggested a classification schema, according to the hierarchical structure of both simulation and optimization models. The authors have further distinguished between "hybrid models" (which they classify) and "hybrid modelling" (previously classified in [11]).…”

Section: Introductionmentioning

confidence: 99%

Hybrid simulation–optimization methods: A taxonomy and discussion

Figueira¹,

Almada-Lobo²

2014

Simulation Modelling Practice and Theory

213

104

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The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields.

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A Unifying View of Hybrid Simulation/Analytic Models and Modeling

Cited by 184 publications

References 29 publications

Network of queues modeling in flexible manufacturing systems : a survey

Network of queues modeling in flexible manufacturing systems : a survey

Hybrid Simulation/Analytic Models for Military Supply Chain Performance Analysis

Hybrid simulation–optimization methods: A taxonomy and discussion

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