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Aiding food security and sustainability efforts through graph neural network-based consumer food ingredient detection and substitution

Scientific reports, 2023-11, Vol.13 (1), p.18809-18809, Article 18809

2023

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Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

IEEE access, 2021-01, Vol.9, p.2734-2757

2021

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Do Autoencoders Need a Bottleneck for Anomaly Detection?

IEEE access, 2022, Vol.10, p.78455-78471

2022

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RFID opportunity analysis for leaner manufacturing

International journal of production research, 2010-01, Vol.48 (9), p.2745-2764

2010

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How does the position of firms in the supply chain affect their performance? An empirical study

Applied network science, 2021-02, Vol.6 (1), p.1-31, Article 19

2021

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Analysing the evolution of aerospace ecosystem development

PloS one, 2020-04, Vol.15 (4), p.e0231985-e0231985

2020

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Topological robustness of the global automotive industry

Logistics research, 2016-12, Vol.9 (1), p.1-18, Article 1

2016

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Reconstructing supply networks

Journal of physic, complexity, 2024-03, Vol.5 (1), p.12001

2024

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A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry

Supply Chain Analytics, 2023-03, Vol.1, p.100003, Article 100003

2023

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Multi-objective optimisation of reliable product-plant network configuration

Applied network science, 2018-01, Vol.3 (1), p.1-1, Article 1

2018

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Exploitation of material consolidation trade-offs in multi-tier complex supply networks

Supply Chain Analytics, 2023-12, Vol.4, p.100050, Article 100050

2023

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Supply network science: Emergence of a new perspective on a classical field

Chaos (Woodbury, N.Y.), 2018-03, Vol.28 (3)

2018

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Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection

Expert systems with applications, 2022-12, Vol.209, p.118196, Article 118196

2022

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Federated machine learning for privacy preserving, collective supply chain risk prediction

International journal of production research, 2023-12, Vol.61 (23), p.8115-8132

2023

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Network science approach for identifying disruptive elements of an airline

Data science and management, 2023-06, Vol.6 (2), p.110-121

2023

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An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization

Computers in industry, 2007-04, Vol.58 (3), p.279-291

2007

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A machine learning approach for predicting hidden links in supply chain with graph neural networks

International journal of production research, 2022-09, Vol.60 (17), p.5380-5393

2022

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Digital Twins: State of the art theory and practice, challenges, and open research questions

Journal of industrial information integration, 2022-11, Vol.30, p.100383, Article 100383

2022

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Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

IEEE transactions on evolutionary computation, 2008-06, Vol.12 (3), p.343-354

2008

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Key Enablers for the Evolution of Aerospace Ecosystems

Journal of aerospace technology and management, 2021-01, Vol.13

2021

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The moderating impact of supply network topology on the effectiveness of risk management

International journal of production economics, 2018-03, Vol.197, p.13-26

2018

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Will bots take over the supply chain? Revisiting agent-based supply chain automation

International journal of production economics, 2021-11, Vol.241, p.108279, Article 108279

2021

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Extracting supply chain maps from news articles using deep neural networks

International journal of production research, 2020-09, Vol.58 (17), p.5320-5336

2020

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Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift

Applied soft computing, 2022-07, Vol.123, p.108912, Article 108912

2022

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The relationship between nested patterns and the ripple effect in complex supply networks

International journal of production research, 2021-01, Vol.59 (1), p.325-341

2021

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Aiding food security and sustainability efforts through graph neural network-based consumer food ingredient detection and substitution

Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

Do Autoencoders Need a Bottleneck for Anomaly Detection?

RFID opportunity analysis for leaner manufacturing

How does the position of firms in the supply chain affect their performance? An empirical study

Analysing the evolution of aerospace ecosystem development

Topological robustness of the global automotive industry

Reconstructing supply networks

A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry

Multi-objective optimisation of reliable product-plant network configuration

Exploitation of material consolidation trade-offs in multi-tier complex supply networks

Supply network science: Emergence of a new perspective on a classical field

Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection

Federated machine learning for privacy preserving, collective supply chain risk prediction

Network science approach for identifying disruptive elements of an airline

An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization

A machine learning approach for predicting hidden links in supply chain with graph neural networks

Digital Twins: State of the art theory and practice, challenges, and open research questions

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

Key Enablers for the Evolution of Aerospace Ecosystems

The moderating impact of supply network topology on the effectiveness of risk management

Will bots take over the supply chain? Revisiting agent-based supply chain automation

Extracting supply chain maps from news articles using deep neural networks

Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift

The relationship between nested patterns and the ripple effect in complex supply networks

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