Journal Information
Computers & Industrial Engineering
https://www.sciencedirect.com/journal/computers-and-industrial-engineering
Impact Factor:
6.700
Publisher:
Elsevier
ISSN:
0360-8352
Viewed:
13277
Tracked:
6
Call For Papers
Industrial engineering is one of the earliest fields to utilize computers in research, education, and practice. Over the years, computers and electronic communication have become an integral part of industrial engineering. Computers & Industrial Engineering (CAIE) is aimed at an audience of researchers, educators and practitioners of industrial engineering and associated fields.

It publishes original contributions on the development of new computerized methodologies for solving industrial engineering problems, as well as the applications of those methodologies to problems of interest in the broad industrial engineering and associated communities. The journal encourages submissions that expand the frontiers of the fundamental theories and concepts underlying industrial engineering techniques.

CAIE also serves as a venue for articles evaluating the state-of-the-art of computer applications in various industrial engineering and related topics, and research in the utilization of computers in industrial engineering education. Papers reporting on applications of industrial engineering techniques to real life problems are welcome, as long as they satisfy the criteria of originality in the choice of the problem and the tools utilized to solve it, generality of the approach for applicability to other problems, and significance of the results produced.

A major aim of the journal is to foster international exchange of ideas and experiences among scholars and practitioners with shared interests all over the world.
Last updated by Dou Sun in 2024-07-13
Special Issues
Special Issue on Redesigning industrial systems supporting a systemic shift towards a Sustainable Circular Economy for achieving Net-Zero
Submission Date: 2024-07-31

Background and motivation Lately, the concept of Circular Economy has gained momentum and is often presented as a way to decouple economic growth from environmental impacts and climate-change (Castro et al., 2022; Kjaer et al., 2019). Effective circular strategies can concur in building a path towards more sustainable production and consumption processes (Acerbi et al., 2022; Acerbi and Taisch, 2020), positively contributing to the decarbonization of industrial systems for achieving the Net-Zero (Nikolaou and Tsagarakis, 2021). However, companies have to face several barriers to the implementation of circular strategies (Chiappetta Jabbour et al., 2020b). Scholars agree that this transition requires a systemic shift that includes the reconfiguration in the design of products, business models, consumption patterns and supply chains (Bressanelli et al., 2022; Kirchherr et al., 2023; Saccani et al., 2023), also through the adoption and exploitation of digital technologies (Chiappetta Jabbour et al., 2020a; Rosa et al., 2020; Sassanelli et al., 2023), while new assessment methods are needed to measure the progresses towards circularity (Elia et al., 2016; Sassanelli et al., 2019). Supply chain collaboration becomes essential for the effective implementation of circular strategies, as companies become more connected through closed loops (Bressanelli et al., 2018; Elia et al., 2020; Sudusinghe and Seuring, 2022). Guest editors: Fabiana Tornese, Università del Salento, Italy, fabiana.tornese@unisalento.it Claudio Sassanelli, Politecnico di Bari, Italy,claudio.sassanelli@poliba.it Gianmarco Bressanelli, Università degli Studi di Brescia, Italy, gianmarco.bressanelli@unibs.it Charbel Jose Chiappetta Jabbour, NEOMA Business School, France, c-j.chiappetta-jabbour@neoma-bs.fr Special issue information: However, many challenges arise in traditionally linear, long, global, complex, and detached supply chains (Taddei et al., 2022). As a result, the systemic shift towards Circular Economy is still not included in companies decarbonization strategies and plans for achieving the net-zero. Industrial systems need to be redesigned considering circularity objectives and constraints, leveraging the exploitation of data to trigger and catalyze decisions on both the management of circular flows of production resources and the adoption of circular practices. Traditional methods and tools (adopted in the old linear perspective to support design, production and logistics processes, to configure business models and organizations, and to plan and actuate strategies), have to leave the room to new ones capable to allow the implementation of circular practices and strategies for achieving the decarbonization of industrial systems. Objectives This special issue aims at investigating the challenges and opportunities connected with the systemic shift towards a Sustainable Circular Economy for the decarbonization of industrial systems. It focuses particularly on the ecosystem (supply chain orchestration, collaboration, and coordination), on enablers (technological innovation, stakeholder engagement, etc.), and on the assessment methods (including impacts on sustainability and long-term perspective). To this end, both practical and theoretical papers are encouraged, based on different research methods (including, but not limited to, case studies, simulation modelling, mathematical modelling, surveys and qualitative research). Several open research areas can be explored in this direction, such as: how to support companies for circular supply chain orchestration and collaboration; how industry 4.0 can support the design of circular business models and the servitization of industrial systems; how to assess the impacts of CE strategies on sustainability, including rebound effects; etc. Topics of interest Authors are encouraged to submit high-quality original empirical, quantitative, qualitative, or conceptual research papers. In detail, topics can include, but are not limited to, the following: Circular supply chain management to set new collaborative circular-driven innovation ecosystems and detect potential strategic actors acting as brokers in these networks. Supply chain orchestration based on the Circular Economy paradigm to support not only the reconfiguration of companies considered as single entities, but also a systemic shift of supply chains with the final aim of optimizing resource efficiency and value maintenance. Innovative integrated production and logistic models for a Circular Economy, bolstering continuous flows along the entire lifecycle of the solutions delivered (from the conception driven by circular design principles up to a dismantling phase connected with the 9Rs strategies). Circular Economy and sustainability assessment, from consolidated methods, such as LCA and Material Flow Analysis (MFA), up to the development of new approaches, frameworks and tools capable to provide new sets of KPIs able to unveil pros and cons of Circular Economy adoption, to assess sustainability in Circular Economy, and also to support the decision making in circular processes. The exploration of the Circular Economy rebound effect phenomenon in the manufacturing domain at all the industrial symbiosis levels (micro, meso, macro) and with both a short and long-run perspective. Sustainable circular business models, mainly affected by the digital servitization phenomenon and capable to exploit technological innovation to improve stakeholders’ engagement in circular ecosystems. Technological innovation for sustainable Circular Economy strategies, implying not only the adoption of ICT technologies in companies processes and organizations, but also the integration and fully employment of digital technologies (belonging to the Industry 4.0 umbrella) to catalyze the servitization of production systems. The link among Industry 4.0 technologies and Circular Economy strategies to bridge the transition of production and logistics infrastructures towards a Sustainable Circular Economy to achieve Net-Zero. Manuscript submission information: The Journal’s submission system is open for submissions to our Special Issue. When submitting your manuscript please select the article type “VSI:Redesigning industrial systems”. Please submit your manuscript before xxx. The submission link is: https://www.editorialmanager.com/caie/default.asp All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles. Submissions will be reviewed according to rigorous standards and procedures through double-blind peer review by at least two qualified reviewers. We invite the authors to ensure that the papers submitted are written in good English standard (American or British usage is accepted, but not a mixture of these). Authors who feel their manuscript might need editing and proofreading may to use the English Language Editing service available from Elsevier's Author Services or other Professional English Scientific Editor, before the submission as well as before the final review. Please see an example here: https://www.sciencedirect.com/journal/computers-and-industrial-engineering Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and link to submit your manuscript is available on the Journal’s homepage at: https://www.sciencedirect.com/journal/computers-and-industrial-engineering/publish/guide-for-authors Submission deadline: 31st July 2024 Inquiries, including questions about appropriate topics, may be sent electronically to GE (Fabiana Tornese) at Email: fabiana.tornese@unisalento.it Keywords: Circular Economy; Industrial Systems; Sustainable Production; decarbonization; Circular supply chains; Industry 4.0
Last updated by Dou Sun in 2024-07-13
Special Issue on http://www.myhuiban.com/journal/issue/id/745
Submission Date: 2024-08-31

The rapid proliferation of networked systems and the ever-increasing demand for reliable and efficient network operations have necessitated a paradigm shift in network monitoring. Traditional network monitoring techniques often struggle to cope with the complexity and scale of modern networks. Machine learning (ML) has emerged as a transformative approach to address these challenges, offering the promise of automating network monitoring, improving anomaly detection, and enhancing overall network performance. Guest editors: Dr. Arne Johannssen University of Hamburg arne.johannssen@uni-hamburg.de Dr. Philipp Otto University of Glasgow Philipp.Otto@glasgow.ac.uk Dr. Pavlo Mozharovskyi Institute Polytechnique de Paris pavlo.mozharovskyi@telecom-paris.fr Special issue information: This SI on “Network Monitoring with Machine Learning Methods” aims to provide a comprehensive platform for the dissemination of cutting-edge research, methodologies, and practical applications in the realm of ML-based network monitoring. With a focus on the integration of ML techniques into network monitoring systems, the SI will encompass a wide range of topics, including but not limited to: 1. Anomaly detection: Exploring ML algorithms for the identification of network anomalies, security breaches, and performance deviations, enhancing the network's ability to respond to threats in real-time. 2. Network traffic analysis: Investigating ML-driven approaches to analyze network traffic patterns, classify data, and optimize Quality of Service (QoS) in network environments. 3. Predictive maintenance: Leveraging ML for predictive maintenance of network infrastructure, ensuring network uptime and reliability through data-driven decision-making. 4. Resource optimization: Developing ML-based methods to allocate network resources dynamically, reducing congestion, and improving the efficiency of network operations. 5. Network security: Enhancing network security through ML-driven intrusion detection, threat identification, and automated response mechanisms. 6. Scalability and performance: Addressing the scalability and performance challenges of ML-powered network monitoring systems to ensure their applicability in large, complex network environments. The SI will bring together researchers, academics, and industry professionals to share their insights, experiences, and innovations in the application of ML to network monitoring. It seeks to foster collaboration and knowledge exchange, pushing the boundaries of what is currently possible in the field of network monitoring. We invite submissions of original research papers, review articles, and case studies that showcase novel approaches, methodologies, and practical implementations of ML in network monitoring. By shedding light on the SOTA techniques and their real-world impact, this SI aims to advance the field of network monitoring and contribute to the ongoing evolution of efficient and secure network operations. Manuscript submission information: Submission instructions The Journal’s submission system is open for submissions to our Special Issue. When submitting your manuscript please select the article type “VSI:Network Monitoring with Machine Learning Methods” so that your article is submitted for the special issue. Please submit your manuscript before 31st August 2024. The submission link is: https://www.editorialmanager.com/caie/default.aspx All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles. Please see an example here: https://www.sciencedirect.com/journal/computers-and-industrial-engineering Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and link to submit your manuscript is available on the Journal’s homepage at : https://www.sciencedirect.com/journal/computers-and-industrial-engineering/publish/guide-for-authors Submission deadline: 30th August 2024 Inquiries, including questions about appropriate topics, may be sent electronically to the guest editors. Keywords: Network Monitoring; Machine Learning; Artificial Intelligence; Anomaly Detection; Network Security; Predictive Maintenance; Network Traffic Analysis; Resource Optimization; Performance Optimization; Intrusion Detection
Last updated by Dou Sun in 2024-07-13
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