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Technologies
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Agentic AI-Driven Optimization in Advanced Manufacturing Systems
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Agentic AI-Driven Optimization in Advanced Manufacturing Systems

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Manufacturing Technology".

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 6536

Editors

Prof. Dr. Ismael Lopez-Juarez

E-Mail Website
Guest Editor
Centre for Research and Advanced Studies (CINVESTAV), Ind. Metalurgica 1062, P. Ind. Ramos Arizpe, Ramos Arizpe 25900, Mexico
Interests: robotics; AI; machine learning; machine vision; human-robot interaction; foundational models; agentic manufacturing; robotic assembly; robot welding
Prof. Dr. Alejandro A. Castillo Atoche

E-Mail Website
Guest Editor
Department of Mechatronics, Autonomous University of Yucatan, Av. Industrias No Contam-Inantes s/n, Cordemex, Merida 97203, Yucatan, Mexico
Interests: intelligent signal processing; wireless sensor networks; Internet of Things; power management; smart materials; composite materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to contribute to this Special Issue on "Agentic AI-Driven Optimization in Advanced Manufacturing Systems", a rapidly evolving field that is transforming traditional reactive agents into autonomous, goal-driven systems capable of learning, adapting, and making independent decisions in complex manufacturing environments.

Artificial intelligence (AI), particularly through the integration of foundational models such as SLMs, LLMs, and VLMs, and combined with the power of GPUs and cloud computing, offers unprecedented generality, scalability, and applicability to a wide range of manufacturing tasks. These advances are reshaping how intelligent agents operate on the shop floor.

However, accomplishing the full potential of AI in manufacturing presents significant challenges, including the following:

  • Data scarcity in unstructured or dynamic environments,
  • High uncertainty arising from sensory variability,
  • Security and safety risks in physical interactions,
  • The need for real-time decision-making in mission-critical processes.
  • Addressing these challenges is key to enabling the shift from reactive automation to truly autonomous manufacturing systems.

This Special Issue seeks to explore the convergence of AI, control systems, robotics, and data analytics to foster intelligent, adaptive, and efficient manufacturing environments. We welcome interdisciplinary contributions from machine learning, industrial engineering, control theory, robotics, and computer science, with a strong emphasis on practical applications and real-world impact.

Suggested Topics (including but not limited to):

  • Autonomous Robots: AI-powered robots that work flexibly and collaborate with humans.
  • Cyber-Physical Systems (CPS): Combining AI with physical systems for real-time control and optimization.
  • Machine Vision and Perception: AI-based computer vision for quality checks and defect detection.
  • Digital Twins: Virtual models of physical systems for simulation and optimization.
  • AI-Driven Optimization: Using AI to adjust manufacturing settings in real-time.
  • Predictive Maintenance: Using data to predict and prevent equipment breakdowns.
  • Supply Chain and Production Planning: AI for managing scheduling, demand, and inventory.
  • Quality Control: Real-time AI systems to ensure product quality.
  • Industry 4.0 Technologies: Using IoT, cloud computing, and big data to improve manufacturing.
  • Human-Machine Collaboration: AI helping humans with decision-making and control.
  • Sustainable and Resilient Manufacturing: AI for reducing waste, saving energy, and adapting to disruptions.
  • Emerging soft robotics applications: Embedded sensing systems are unlocking innovative applications in areas such as human–machine interfaces, industrial automation, and more. These advancements demonstrate the transformative potential of AI-driven soft robotics across multiple domains.

We look forward to your contributions that advance the field of intelligent, agentic AI in manufacturing. 

Prof. Dr. Ismael Lopez-Juarez
Prof. Dr. Alejandro A. Castillo Atoche
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies is an international peer-reviewed open access monthly 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 1800 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

  • agentic AI
  • AI-driven optimization
  • collaborative robots (Cobots)
  • cyber-physical systems (CPS)
  • deep learning
  • digital twins
  • foundational models (SLMs, LLMs, VLMs)
  • Industry 4.0
  • machine vision
  • predictive maintenance
  • quality control
  • real-time decision making
  • reinforcement learning (RL)
  • soft robotics
  • supply chain optimization
  • sustainable manufacturing

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Published Papers (3 papers)

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Research

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33 pages, 2341 KB  
Article
Digital Twin-Based Hybrid Simulation–Prediction Framework for KPI Optimization in Sustainable Digital Printing
by Diana Bratić, Suzana Pasanec Preprotić, Hrvoje Cajner and Branimir Preprotić
Technologies 2026, 14(3), 170; https://doi.org/10.3390/technologies14030170 - 10 Mar 2026
Cited by 1 | Viewed by 2517
Abstract
The increasing emphasis on sustainability in digital printing requires quantitative methods for optimizing key performance indicators (KPIs) under technical and operational constraints. The term digital twin is used here in a methodological and analytical sense, as a simulation framework for analyzing interdependence, prediction, [...] Read more.
The increasing emphasis on sustainability in digital printing requires quantitative methods for optimizing key performance indicators (KPIs) under technical and operational constraints. The term digital twin is used here in a methodological and analytical sense, as a simulation framework for analyzing interdependence, prediction, and multi-criteria optimization of KPIs, rather than as a direct virtual replica of a specific physical production system. This paper proposes a hybrid simulation–prediction model based on a digital twin framework for optimization of KPIs in sustainable digital printing, with particular emphasis on overall equipment effectiveness (OEE). Due to the limited availability of structured industrial data, the model is developed using a synthetically generated dataset constructed in accordance with industry-reported operating ranges and technically realistic digital printing process variables. Random Forest and XGBoost algorithms are applied to model nonlinear relationships between process parameters and KPIs, including material waste, energy consumption, machine downtime, and OEE. Based on these predictive models, a constrained multi-objective optimization procedure is performed to identify Pareto-efficient configurations that reduce material waste and energy consumption while maintaining acceptable downtime and OEE levels. The results characterize structural trade-offs among environmental and operational KPIs within a formally defined decision space. Full article
(This article belongs to the Special Issue Agentic AI-Driven Optimization in Advanced Manufacturing Systems)
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Review

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28 pages, 1899 KB  
Review
A Conceptual Study for Cognitive Bias Amplification in Agentic AI-Driven Business Processes, Management, and Intelligence
by Subhra Mondal, Subhankar Das and Vasiliki G. Vrana
Technologies 2026, 14(7), 415; https://doi.org/10.3390/technologies14070415 (registering DOI) - 7 Jul 2026
Abstract
Agentic artificial intelligence (AAI) and retrieval-augmented generation (RAG) are increasingly embedded in organisational business intelligence (BI) and business process management (BPM). Unlike conventional AI, these systems set sub-goals, plan multi-step workflows, and retrieve and store information under limited human supervision. This article argues [...] Read more.
Agentic artificial intelligence (AAI) and retrieval-augmented generation (RAG) are increasingly embedded in organisational business intelligence (BI) and business process management (BPM). Unlike conventional AI, these systems set sub-goals, plan multi-step workflows, and retrieve and store information under limited human supervision. This article argues that such autonomy not only transmits human cognitive bias into organisational decisions but also amplifies that bias under identifiable conditions. We develop the Bias Amplification Model (BAM), a three-layer account of how bias enters and escalates. In the injection layer, human biases enter through goal framing, prompt design, and data scoping. In the propagation layer, the agent compounds these biases across autonomous execution steps. In the crystallisation layer, RAG memory encodes biassed outputs as retrievable organisational knowledge that later cycles treat as evidence. A feedback loop links the three layers, converting episodic human bias into structural organisational bias. We state seven propositions, specify amplifying and mitigating conditions, and ground the model in a PRISMA-guided synthesis of 47 studies. A controlled, replicated experiment with an autonomous agent in a supplier-selection pipeline provides initial empirical support for injection, propagation, the mitigating effect of a single governance checkpoint, and bias transmission across a task boundary via retrieved memory. The framework reframes AI-driven process optimisation as a possible source of silent decline in decision quality. Full article
(This article belongs to the Special Issue Agentic AI-Driven Optimization in Advanced Manufacturing Systems)
38 pages, 5061 KB  
Review
Mapping the Industry 5.0 Landscape: Enabling Technologies, Human-Centered Systems, Sectoral Applications, and SDG Alignment—A PRISMA-ScR Review
by Patricia Acosta-Vargas, Luis Suarez, Tomas Cuadrado and Luis Salvador-Ullauri
Technologies 2026, 14(5), 268; https://doi.org/10.3390/technologies14050268 - 29 Apr 2026
Viewed by 2739
Abstract
Industry 5.0 is no longer understood merely as an extension of automation; it reflects a broader shift toward integrating technological advancement with human well-being, sustainability, and resilience. However, the literature reveals a fragmented landscape in which technological, industrial, and ecological dimensions are often [...] Read more.
Industry 5.0 is no longer understood merely as an extension of automation; it reflects a broader shift toward integrating technological advancement with human well-being, sustainability, and resilience. However, the literature reveals a fragmented landscape in which technological, industrial, and ecological dimensions are often treated separately, hindering a cohesive understanding of the paradigm. To address this gap, this study conducts a PRISMA-ScR-based review of 52 peer-reviewed studies (January 2021–March 2026), structured around ten research questions that examine technologies, sectors, methods, human-centered design, sustainability alignment, and implementation barriers. The review demonstrates high reliability (Cohen’s κ = 0.981). Findings highlight artificial intelligence (86%), collaborative robotics (80%), IoT (71%), and digital twins (63%) as core technologies, typically integrated within human-in-the-loop systems. Manufacturing and healthcare lead adoption, reporting reduced physical workload and improved safety. Nonetheless, only 63% of studies explicitly align with sustainability frameworks, revealing a persistent gap. Thus, inclusive Industry 5.0 remains a promising yet still insufficiently consolidated concept. Full article
(This article belongs to the Special Issue Agentic AI-Driven Optimization in Advanced Manufacturing Systems)
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