Simulation optimization: A panel on the state of the art in research and practice

Fu, Michael C.; Bayraksan, Güzi̇n; Henderson, Shane G.; Nelson, Barry L.; Powell, Warren B.; Ryzhov, Ilya O.; Thengvall, Benjamin

doi:10.1109/wsc.2014.7020198

Cited by 18 publications

(6 citation statements)

References 9 publications

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“…The commercial success of OptQuest™ can be attributed partly to its integration with several popular simulation tools. However due to semantic differences between OptQuest and simulation, integrating OptQuest with each simulation platform is an expensive process that does not scale well to many-solver environments [10].…”

Section: Use Cases For Dels Analysis Interoperabilitymentioning

confidence: 99%

A model-driven approach to interoperability between simulation and optimization for production and logistics systems

Sprock¹

2020

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Simulation and optimization must be used together to design and operate large-scale, complex systems. Integrating them presents a number of conceptual and technical problems, which can be addressed with more expressive, computer-interpretable system models. This paper presents a model-driven approach to integrating simulation and optimization methods by exchanging system designs that specialize analysis abstractions, both defined in the Systems Modeling Language (SysML). The analysis abstractions enable translation of system models to corresponding analysis models and tool interfaces. This approach (model-driven system-analysis integration) is demonstrated by developing a multi-fidelity, multi-method simulation optimization methodology and applying it to a supply chain design case study.

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Section: Use Cases For Dels Analysis Interoperabilitymentioning

confidence: 99%

A model-driven approach to interoperability between simulation and optimization for production and logistics systems

Sprock¹

2020

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“…Optimization is another technique that can be combined with DES to define optimal input control variables, e.g., production capacity. Each iteration of an optimization requires multiple simulation runs for a set of system parameters [26,41,55,64].…”

Section: Introductionmentioning

confidence: 99%

Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods

Chiang¹,

Lin²,

Long³

2020

Operations Research Perspectives

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Uncertainty propagation of large scale discrete supply chains can be prohibitive when a large number of events occur during the simulated period and discrete event simulations (DES) are costly. We present a time bucket method to approximate and accelerate the DES of supply chains. Its stochastic version, which we call the L(logistic)-leap method, can be viewed as an extension of the leap methods, e.g., τ -leap [34], Dleap [6], developed in the chemical engineering community for the acceleration of stochastic DES of chemical reactions. The L-leap method instantaneously updates the system state vector at discrete time points and the production rates and policies of a supply chain are assumed to be stationary during each time bucket.We propose to use Multilevel Monte Carlo (MLMC) to efficiently propagate the uncertainties in a supply chain network, where the levels are naturally defined by the sizes of the time buckets of the simulations.We demonstrate the efficiency and accuracy of our methods using four numerical examples derived from a real world manufacturing material flow. In these examples, our multilevel L-leap approach can be faster than the standard Monte Carlo (MC) method by one or two orders of magnitudes without compromising the accuracy.

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“…The recent availability of mature and efficient SOSO methods, coupled with the ubiquitous availability of parallel computing power, makes characterizing the efficient set as the solution to a MOSO problem seem like an increasingly realistic goal. Members of the Monte Carlo simulation community have expressed interest in advancing this literature (Fu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

An Introduction to Multiobjective Simulation Optimization

Hunter

Applegate

Arora

et al. 2019

ACM Trans. Model. Comput. Simul.

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The multiobjective simulation optimization (MOSO) problem is a nonlinear multiobjective optimization problem in which multiple simultaneous and conflicting objective functions can only be observed with stochastic error. We provide an introduction to MOSO at the advanced tutorial level, aimed at researchers and practitioners who wish to begin working in this emerging area. Our focus is exclusively on MOSO methods that characterize the entire efficient or Pareto-optimal set as the solution to the MOSO problem; later, this set may be used as input to the broader multicriteria decision-making process. Our introduction to MOSO includes an overview of existing theory, methods, and provably convergent algorithms that explicitly control sampling error for (1) MOSO on finite sets, called multiobjective ranking and selection; (2) MOSO with integer-ordered decision variables; and (3) MOSO with continuous decision variables. In the context of integer-ordered and continuous decision variables, we focus on methods that provably converge to a local efficient set under the natural ordering. We also discuss key open questions that remain in this emerging field.

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Simulation optimization: A panel on the state of the art in research and practice

Cited by 18 publications

References 9 publications

A model-driven approach to interoperability between simulation and optimization for production and logistics systems

A model-driven approach to interoperability between simulation and optimization for production and logistics systems

Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods

An Introduction to Multiobjective Simulation Optimization

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