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All Journal Journal of Industrial Engineering & Management Research (JIEMAR) Journal of Exploratory Dynamic Problems
Chukwumuanya, Emmanuel Okechukwu
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Religion Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Machine Learning Applications for Production Scheduling Optimization Sunday, Aguh Patrick; Emeka, Udu Chukwudi; Chukwumuanya, Emmanuel Okechukwu; Chikwendu, Okpala Charles
Journal of Exploratory Dynamic Problems Vol. 2 No. 4 (2025): Vol.2 No.4 2025
Publisher : Fakultas Keguruan dan Ilmu Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/edp.v2i4.137

Abstract

Production scheduling represents a critical function within manufacturing and industrial operations, exerting a direct influence on productivity, operational efficiency, and overall cost management. Traditional scheduling methodologies, while foundational, often exhibit limitations when confronted with the complexity, variability, and dynamic demands of contemporary production environments. In response, this paper investigates the potential of Machine Learning (ML) techniques for the enhancement of production scheduling outcomes. Specifically, it examines the capabilities of reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to real-time disruptions, and support more effective decision-making processes. The paper further reviews notable industrial applications of these techniques, critically evaluating their performance relative to conventional methods. In addition, it addresses the inherent challenges associated with the deployment of ML in production scheduling, including data availability, algorithmic interpretability, and integration with legacy systems. Finally, the study outlines future research directions, emphasizing the need for more robust, scalable, and interpretable ML-based scheduling solutions to meet the evolving demands of modern industry.
Agile project management and emerging technologies in concurrent engineering for sustainable and collaborative product design Anyaora, Sunday Chimezie; Chukwumuanya, Emmanuel Okechukwu; Dara, Jude Ezechi; Ofochebe, Sunday Madubueze; Okoye, Chibuzo Ndubuisi
Journal of Industrial Engineering & Management Research Vol. 6 No. 3 (2025): June 2025
Publisher : AGUSPATI Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7777/jiemar.v6i3.595

Abstract

Designing sustainable and innovative products today requires more than just good ideas as it demands speed, flexibility, teamwork, and smart use of technology. This study used a systematic review approach guided by PRISMA to examine how Agile project management and emerging technologies enhance concurrent engineering for sustainable and collaborative product design. Literature was sourced from five major databases using targeted search terms. After a rigorous three-phase screening and quality appraisal, relevant studies were thematically synthesized around Agile, AI, automation, cloud platforms, ethics, circular economy, and composite manufacturing. The result showed that revealed that integrating Agile project management into concurrent engineering enhances flexibility, collaboration, and design responsiveness. Design automation and AI tools improve accuracy and decision-making, while cloud-based platforms strengthen real-time teamwork. Generative design supports creativity and rapid iteration. Ethical concerns like transparency and inclusivity emerged as essential. Circular economy strategies help reduce waste and extend product life. In composite manufacturing, concurrent engineering and group design improve material efficiency, product quality, and speed, demonstrating strong potential for sustainable innovation. The findings suggest that integrating Agile practices with emerging technologies creates a powerful framework for building smarter, greener, and more collaborative products now and in the future.
Co-Authors Anyaora, Sunday Chimezie Chikwendu, Okpala Charles Dara, Jude Ezechi Emeka, Udu Chukwudi Ofochebe, Sunday Madubueze Okoye, Chibuzo Ndubuisi Sunday, Aguh Patrick