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A Method for Robustness Optimization Using Generative Adversarial Networks

Published: 15 June 2020 Publication History

Abstract

This paper presents an approach for optimizing the robustness of production and logistic systems based on deep generative models, a special method of deep learning. Robustness here refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has a minimal effect on given output parameters. In a case study, the proposed method is tested and compared to a traditional method for robustness analysis. The basic idea is to use deep neural networks to generate data for experiment plans and rate them by use of a simulation model of the production system. We propose to use two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors, respectively, in a competitive, turn-based game. In one turn, the controllable factors are optimized and the noise remains constant, and vice versa in the next turn. For the calculations of the robustness, the planned experiments are conducted and rated using a simulation model in each learning step.

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  • (2022)A Method Using Generative Adversarial Networks for Robustness OptimizationACM Transactions on Modeling and Computer Simulation10.1145/350351132:2(1-22)Online publication date: 4-Mar-2022

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  1. A Method for Robustness Optimization Using Generative Adversarial Networks

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      cover image ACM Conferences
      SIGSIM-PADS '20: Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
      June 2020
      204 pages
      ISBN:9781450375924
      DOI:10.1145/3384441
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      Published: 15 June 2020

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      Author Tags

      1. deep learning
      2. generative adversarial networks
      3. machine learning
      4. robustness optimization

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      • (2022)A Method Using Generative Adversarial Networks for Robustness OptimizationACM Transactions on Modeling and Computer Simulation10.1145/350351132:2(1-22)Online publication date: 4-Mar-2022

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