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Big Data Systems Embedded with Intelligence Algorithms for Sustainable Operations...
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Big Data Systems Embedded with Intelligence Algorithms for Sustainable Operations Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 9477

Special Issue Editors

Prof. Dr. Felix T. S. Chan

E-Mail Website
Guest Editor
Department of Decision Sciences, School of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao
Interests: engineering management; logistics; supply chain management; production management systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junhu Ruan

E-Mail Website
Guest Editor
College of Economics and Management, Northwest A&F University, Yangling 712100, China
Interests: logistics management; data mining techniques and digital operations
Prof. Dr. Ting Qu

E-Mail Website
Guest Editor
School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), Zhuhai 519070, China
Interests: logistics and supply chain management; management and control of intelligent manufacturing systems

Special Issue Information

Dear Colleagues,

With the development of cloud computing, the Internet of Things, Industry 4.0, and Internet plus, various data from all sectors are collected and stored in real time, which brings in a completely new era in the Big Data environment. The characteristics of Big Data, such as their huge volume, ability to provide real-time updates, and diversity, are challenging the existing traditional models and algorithms in the literature. Operation management is one of the key areas Big Data have been continuously addressing due to their main role and major contribution.

The introduction of the advanced features of Big Data will downgrade the models and algorithms of computing, optimization, and control in classical systems. In this regard, scholars and practitioners are making great efforts to develop new ways and tools for dealing with the advancement of Big Data systems. We obviously have to extend or reformulate extant models and intelligence algorithms to solve the computing, optimization, and control issues in the fast development trend of Big Data systems.

This Special Issue aims to present innovative models with embedded intelligence algorithms for dealing with the computing, optimization, and control issues in Big Data systems. It also aims at promoting exchanges and interactions between investigators across different fields in operation management. We solicit high-quality, original research or review articles focused on models and intelligence algorithms for the computing, optimization, and control issues in Big Data systems. Potential topics include but are not limited to:

  • Industrial Big Data;
  • Big Data in operations management;
  • Big Data representation and extraction;
  • Cybersecurity and data integrity for Big Data systems;
  • Data quality;
  • Privacy protection for Big Data systems.
  • Data scenario and sample scarcity;
  • Domain knowledge and data visualization;
  • Storage paradigms of Big Data;
  • Innovative computing based on Big Data;
  • Model formulation for Big Data systems;
  • Optimization algorithms based on Big Data;
  • Data-driven control models and intelligence algorithms;
  • Healthcare and Big Data;
  • Intelligent transportation and Big Data;
  • Urban development and Big Data;
  • Smart city and Big Data;
  • Smart operations and Big Data;
  • Disaster management and Big Data.

Prof. Dr. Felix T. S. Chan
Prof. Dr. Junhu Ruan
Prof. Dr. Ting Qu
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 submissions that pass pre-check are 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. Sustainability 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 2400 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

  • industrial big data
  • data quality
  • model formulation
  • intelligence algorithms
  • cyber-security
  • data integrity
  • domain knowledge
  • data visualization
  • data-driven control models
  • privacy protection

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Published Papers (3 papers)

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Research

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23 pages, 1497 KiB  
Article
Understanding the Effect of Multi-Agent Collaboration on the Performance of Logistics Park Projects: Evidence from China
by Dan Yang, Weili Yin, Sen Liu and Felix T. S. Chan
Sustainability 2022, 14(7), 4179; https://doi.org/10.3390/su14074179 - 31 Mar 2022
Cited by 3 | Viewed by 2491
Abstract
With the rapid development of a new generation of information technology and data systems, there are more and more modern logistics park projects appearing in China. However, in the process of the construction and operation of a logistics park project, there is often [...] Read more.
With the rapid development of a new generation of information technology and data systems, there are more and more modern logistics park projects appearing in China. However, in the process of the construction and operation of a logistics park project, there is often a lack of coordination between the multiple parties (usually the government, the development enterprise and the entered enterprises), which leads to a series of problems such as low efficiency and disordered management and so on, finally affecting the project performance. However, few studies have focused on this phenomenon, and prior studies are unclear regarding the impact of multi-agent collaboration on logistics park project performance. Therefore, in this study, we investigate the link between multi-agent collaboration and the logistics park project performance based on survey data from Yunnan Province in China. The empirical analysis was conducted using the partial least squares (PLS)-based structural equation modeling with Smart PLS 2.0. The data analysis results suggest that the three dimensions of multi-agent collaboration (management, mechanism and information collaboration) have a significant positive impact on the performance of a logistics park project. Under different environmental dynamics conditions, different strategies should be adopted by a logistics park project to improve the performance. This study provides a new perspective for understanding the value of multi-agent collaboration in logistics park projects both in theory and practice. Full article
(This article belongs to the Special Issue Big Data Systems Embedded with Intelligence Algorithms for Sustainable Operations Management)
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17 pages, 2186 KiB  
Article
A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm
by Quande Dong, Cui Wang, Shitong Peng, Ziting Wang and Conghu Liu
Sustainability 2021, 13(16), 9015; https://doi.org/10.3390/su13169015 - 12 Aug 2021
Cited by 13 | Viewed by 2036
Abstract
The flue gas desulfurization process in coal-fired power plants is energy and resource-intensive but the eco-efficiency of this process has scarcely been considered. Given the fluctuating unit load and complex desulfurization mechanism, optimizing the desulfurization system based on the traditional mechanistic model poses [...] Read more.
The flue gas desulfurization process in coal-fired power plants is energy and resource-intensive but the eco-efficiency of this process has scarcely been considered. Given the fluctuating unit load and complex desulfurization mechanism, optimizing the desulfurization system based on the traditional mechanistic model poses a great challenge. In this regard, the present study optimized the eco-efficiency from the perspective of operating data analysis. We formulated the issue of eco-efficiency improvement into a many-objective optimization problem. Considering the complexity between the system inputs and outputs and to further reduce the computational cost, we constructed a Kriging model and made a comparison between this model and the response surface methodology based on two accuracy metrics. This surrogate model was then incorporated into the NSGA-III algorithm to obtain the Pareto-optimal front. As this Pareto-optimal front provides multiple alternative operating options, we applied the TOPSIS to select the most appropriate alternative set of operating parameters. This approach was validated using the historical operation data from the desulfurization system at a coal-fired power plant in China with a 600 MW unit. The results indicated that the optimization would cause an improvement in the efficiency of desulfurization and energy efficiency but a slight increase in the consumption of limestone slurry. This study attempted to provide an effective operating strategy to enhance the eco-efficiency performance of desulfurization systems. Full article
(This article belongs to the Special Issue Big Data Systems Embedded with Intelligence Algorithms for Sustainable Operations Management)
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Review

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15 pages, 3736 KiB  
Review
Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace
by Jingyi Zhang, Jiaxin Liu, Yaqi Chen, Xiaochun Feng and Zilai Sun
Sustainability 2021, 13(14), 7662; https://doi.org/10.3390/su13147662 - 8 Jul 2021
Cited by 15 | Viewed by 3845
Abstract
With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated [...] Read more.
With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years. Full article
(This article belongs to the Special Issue Big Data Systems Embedded with Intelligence Algorithms for Sustainable Operations Management)
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