Complex networks of material flow in manufacturing and logistics: Modeling, analysis, and prediction using stochastic block models

Funke, Thorben; Becker, Till

doi:10.1016/j.jmsy.2020.06.015

Cited by 17 publications

(6 citation statements)

References 90 publications

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“…Petros and Ken came up with three methods of developing the Bayesian networks employed for spare parts demand forecasting [40]. orben and Till investigated how a network model with a stochastic block of interconnections was applied to model and predict material flows in manufacturing systems [41]. In particular, Pani et al presented some preliminary results obtained using data mining and proposed a classification and regression trees model to reduce the range of uncertainty of ship arrivals in port in the past [42].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

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Section: Literature Review and Related Workmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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“…A further development in network modeling in manufacturing was the introduction of stochastic models. In particular, the Stochastic Block Model (SBM) allows for a prediction of future states in a manufacturing system (Funke and Becker 2020). The motivation behind the application of SBMs as a tool in manufacturing systems modeling is the fact that many material ïň Ćow systems consist of elements that can be grouped into clusters of similar objects in terms of their role in the material flow, e.g., manufacturing cells.…”

Section: Advanced Network Modeling: Stochastic Block Modelsmentioning

confidence: 99%

“…Funke and Becker (2019) have investigated, compared, and evaluated a variety of SBM variants and inference methods to facilitate the selection process. Funke and Becker (2020) were then able to demonstrate how an SBM can be applied to perform link prediction. Due to the stochastic nature of the model, it is possible to retrieve a probability of two nodes being connected (or not being connected) in the future.…”

Section: Advanced Network Modeling: Stochastic Block Modelsmentioning

confidence: 99%

Complex Networks in Manufacturing and Logistics: A Retrospect

Becker

Wagner-Kampik

2021

Dynamics in Logistics

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The methodology to model systems as graphs or networks already exists for a long time. The availability of information technology and computational power has led to a renaissance of the network modeling approach. Scientists have collected data and started to create huge models of complex networks from various domains. Manufacturing and logistics benefits from this development, because material flow systems are predetermined to be modeled as networks. This chapter revisits selected advances in network modeling and analysis in manufacturing and logistics that have been achieved in the last decade. It presents the basic modeling concept, the transition from static to dynamic and stochastic models, and a collection of examples how network models can be applied to contribute to solving problems in planning and control of logistic systems.

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“…Production processes are central to organized societies and have thus been extensively studied in complex systems and complex networks literature from various viewpoints: interfirm supply-chain [1][2][3], trade [4][5][6], material flow analysis [7]. Recent works use block models to reconstruct an interfirm network [8][9][10][11] from partial knowledge, or model manufacturing process below the factory level [12].…”

Section: Introductionmentioning

confidence: 99%

Production process networks: a trophic analysis

Hazan

2023

J. Phys. Complex.

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In this article, production process databases originating from environmental sciences, more speciﬁcally from Life Cycle Inventory (LCI), are considered as bipartite directed random networks. To model the observed directed hierarchical connection patterns, we turn to recent development concerning trophic coherence. Extending the scope to include bipartite networks, we compare several LCI networks to networks from other ﬁelds, and show empirically that they have high coherence and belong to the loopless regime, or close to its boundary.

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Complex networks of material flow in manufacturing and logistics: Modeling, analysis, and prediction using stochastic block models

Cited by 17 publications

References 90 publications

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

Complex Networks in Manufacturing and Logistics: A Retrospect

Production process networks: a trophic analysis

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