Computers and Industrial Engineering

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	6.5

SJR (SCImago Journal Rank)

Year	Value
2024	1.628

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	176

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Decision Sciences and Engineering, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Aquila Optimizer: A novel meta-heuristic optimization algorithm

  Citation: 1814

  Authors: Laith, Dalia, Mohamed, Ahmed A., Mohammed A.A., Amir H.

• African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

  Citation: 1113

  Authors: Benyamin, Farhad Soleimanian, Seyedali

• A review of applications in federated learning

  Citation: 1067

  Authors: Li, Yuxi, Mike, Kuo-Yi

• A systematic literature review of machine learning methods applied to predictive maintenance

  Citation: 973

  Authors: Thyago P., Fabrízzio A. A. M. N., Roberto, Roberto da P., João P., Symone G. S.

• Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)

  Citation: 964

  Authors: Željko, Dragan, Adis, Prasenjit

• The vehicle routing problem: State of the art classification and review

  Citation: 904

  Authors: Kris, Katrien, Inneke

• The use of grey relational analysis in solving multiple attribute decision-making problems

  Citation: 885

  Authors: Yiyo, Taho, Guan-Wei

• Predictive maintenance in the Industry 4.0: A systematic literature review

  Citation: 856

  Authors: Tiago, Cristiano André, Rodrigo, Miromar José, Eduardo Silveira, Guann Pyng

• A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements

  Citation: 819

  Authors: E., K.

• A survey on new generation metaheuristic algorithms

  Citation: 720

  Authors: Tansel, Ender, Tayfun, Ahmet

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