Special Issue "Big Data and AI for Process Innovation in the Industry 4.0 Era, Volume II"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 January 2022).
Related Special Issue: Big Data and AI for Process Innovation in the Industry 4.0 Era

Special Issue Editors

Prof. Dr. Hyerim Bae
E-Mail Website
Guest Editor
Department of Industrial Engineering, Pusan National University, Busan 46241, Korea
Interests: big data analysis; process science; AI and applications; smart port; logistics information systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jaehun Park
E-Mail Website
Guest Editor
Department of Business Administration, Changwon National University, Changwon 51140, Korea
Interests: smart factory; big data analysis; performance evaluation; multi-criteria decision-making

Special Issue Information

Dear Colleagues,

With the rapid development of innovative technologies, such as artificial intelligence (AI), big data, Internet of Things, and cloud computing, the new concept of Industry 4.0 has been revolutionizing production and logistics systems by introducing distributed, collaborative, and automated processes. The objective of Industry 4.0 is a drastic enhancement of productivity, which depends on the processes of the enterprise. In order to innovate processes, big data and AI have been considered key solutions. Big data analytics is a process of examining data to discover knowledge, such as unknown patterns and correlations, market insights, and customer preferences, which can be useful to make various business decisions. Significant advances in deep learning, machine learning, and data mining have improved to the point where these techniques can be used in analyzing big data in any kind of industry. Big data is also recognized as a fundamental technology for advancing AI with sophisticated algorithms and advanced computing power. In this sense, big data and AI are becoming core assets of Industry 4.0 and process innovation. Thus, we invite academic communities and relevant industrial partners to submit papers on “Big Data and AI for Process Innovation in the Industry 4.0 Era” to this Special Issue. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Operational big data analytics;
  • AI and big data applications for Industry 4.0;
  • AI and big data for smart port and logistics;
  • Algorithms for process analysis;
  • Reinforcement learning for real-time decision-making;
  • Cloud computing and IoT for operational intelligence;
  • Deep learning for business intelligence and data mining;
  • Cyber physical systems and cyber-physical production systems;
  • Advanced manufacturing and smart factories;
  • Advanced data mining and process mining;
  • Process modeling and simulation;
  • Industrial Internet of Things;
  • Performance analysis of automated systems.

Prof. Dr. Hyerim Bae
Prof. Dr. Jaehun Park
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • process analysis
  • big data
  • AI
  • Industry 4.0
  • manufacturing and logistics process

Published Papers (2 papers)

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Research

Article
Resource Capacity Requirement for Multi-Terminal Cooperation in Container Ports
Appl. Sci. 2021, 11(19), 9156; https://doi.org/10.3390/app11199156 - 01 Oct 2021
Viewed by 336
Abstract
Capacity sharing among neighboring terminals offer a means to meet increasing or unexpected demand for cargo-handling without additional capital investment. This study proposes a model for capacity requirement planning of major resources, such as quay cranes (QCs), storage space, and gate, in multiterminal [...] Read more.
Capacity sharing among neighboring terminals offer a means to meet increasing or unexpected demand for cargo-handling without additional capital investment. This study proposes a model for capacity requirement planning of major resources, such as quay cranes (QCs), storage space, and gate, in multiterminal port operations where demand is time dependent. A resource profile simulation is run to generate random events across the terminals and estimate the capacity requirement in the form of workload distributions on port resources over time-shifts. The effects on workload requirement, arising from multiterminal cooperation, are subsequently evaluated in consideration of different container flows among terminals. Experimental results suggest that higher transferring rate between terminals will reduce the QC intensity and storage space requirements but increase gate congestion. Variabilities in the QC intensity and storage space requirements also increase due to shorter stays and more movements in container inventory at the yard. The interaction effect between transferring and trans-shipment rates further shows that the average resource requirements for a terminal can be greatly reduced when the interterminal transferring of containers contributes positively to a more even workload redistribution across terminals. The most significant improvements occur when trans-shipment rate is 85% and transferring rate is 75% for QC intensity; trans-shipment rate is 90% and transferring rate is 60% for storage capacity; and trans-shipment rate is 80% and transferring rate is 75% for gate congestion. Full article
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Article
Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
Appl. Sci. 2021, 11(19), 8995; https://doi.org/10.3390/app11198995 - 27 Sep 2021
Viewed by 336
Abstract
The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such [...] Read more.
The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression. However, these methods do not explain the complexity and variability of data caused by changes in the external environment, such as the global financial crisis and economic fluctuations. Deep learning can explore the inherent patterns of data and analyze the characteristics (time series, external environmental variables, and outliers); hence, the accuracy of deep learning-based volume prediction models is better than that of traditional models. However, this does not include the study of overall trends (upward, steady, or downward). In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. The proposed model explores external variables that are related to container volume, combining port volume time-series decomposition with external variables and deep learning-based multivariate long short-term memory (LSTM) prediction. The results indicate that the proposed model performs better than the traditional LSTM model and follows the trend simultaneously. Full article
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