Advancements and the adoption of productive human-robot collaborations have im pacted every aspect of modern manufacturing. Although automation has been increasingly utilized on the shop floor for decades, a significant portion of manufacturing operations remains manual primarily due to the dominating ability of human operators to perform these operations well, compared to robots. These manual or semi-automated operations involve timely complex manipulations and reasoning, and depend highly on human labor skills, intelligence, and expertise. To prepare the workforce of the future, manufacturers are increasingly turning to augmented reality (AR) to prepare, train, guide, and refine the skills of their less-experienced workers by allowing skilled workers to collaboratively guide and mentor less-experienced workers, collaboratively and on the fly. In this paper, we propose a new skill- and- knowledge- -sharing model in manufacturing systems with a hub for collaborative intelligence (HUB-CI) that supports an effective learning protocol. The learning curves of operators are modeled as a function of time and the complexity of skills and knowledge, and analyzed for the learning quality. A case- study of assembly kitting workflow is used to conduct the experimental simulations and analyses. Several insights are drawn from the preliminary simulation analyses to inform the design of future AR technologies for skill and knowledge sharing among manufacturing workers, working collaboratively with robots on complex manipulation and reasoning tasks.

Collaborative automation, which combines humans and robots in a shared cyber-physical workspace, has emerged as a promising approach to address the increasing complexity of tasks and the need for greater efficiency in manufacturing activities. This article explores the current state of the art, motivations, and potential benefits of collaborative automation, drawing on insights from recent research in three different directions: collaborative robotics, collaborative digital management, and cyber-augmented, including artificial intelligence (AI) and augmented reality (AR), human workers' and teams' collaboration. This article aims to provide a basis of understanding between the research and industry implementation of collaborative automation. By examining how complementary strengths of humans and robots are leveraged, the study aims to unlock future possibilities for automation in cyber-physical manufacturing systems, leading to greater efficiency, flexibility, and operational safety.

This research explores integrating blockchain technology with digital twins in semiconductor manufacturing and its impact on supply chain optimization. Drawing insights from leading work such as "Blockchain Revolution" and "The Business Blockchain", this paper delves into interrelated models, processes, and protocols, highlighting the potential for transforming semiconductor manufacturing and supply. An implementation case study is examined.

The use of innovative technologies and the integration of efficient human-robot interaction have had a significant influence on all facets of modern manufacturing. While automation has advanced, numerous tasks still depend on human operators due to their superior dexterity and reasoning abilities. Augmented reality (AR) has emerged as a critical enabler, enhancing the skills of less-experienced workers, improving operational resilience, and enabling rapid adaptation to disruptions. This study presents a novel skill-and-knowledge-sharing paradigm for manufacturing systems. The architecture, called HUB-CI (a hub for collaborative intelligence), facilitates an efficient learning protocol. The model uses quantified learning curves to evaluate operator performance over time and incorporates AR-driven skill and knowledge sharing to reduce response times, optimize scheduling makespan, and improve resilience metrics such as system stability, adaptability, and recovery speed under disruptive conditions. Numerical experiments reveal that HUB-CI significantly decreases response times and improves scheduling makespan by up to 20.52 % after 500 iterations, (p-value <0.01) and achieves consistent performance gains under disruptive conditions, due to its ability to maintain makespan robustness within 5 % of the non-disrupted baseline. In more stable environments with converging agent skill levels, the model sustains strong performance, demonstrating its versatility. By integrating quantified learning curves with four core resilience metrics-adaptability, collaboration and visibility, robustness, and sustainability-HUB-CI enables adaptive task allocation and real-time collaboration, offering a substantial advancement in cyber-collaborative production systems.

Supply network resilience and responsiveness have become a critical topic due to multiple global supply network disruptions in recent years, ranging from natural to human-made hazards, e.g., earthquakes, the COVID-19 pandemic, and piracy in the Gulf of Africa. This research addresses two key aspects of overcoming these challenges: information sharing and supply network trust. Supply network participants need to be willing to share information in time and be able to anticipate and respond appropriately to disruptions. After introducing an extension to the Collaborative Auction Protocol (CAP), we present its framework, application, and validation. Next, we explore Demand-and-capacity sharing in supply networks, building on prior research (Yoon and Nof, 2010; Ajidarma et al., 2022). Demand-and-capacity sharing was chosen as an implementation test for CAP research. One critical gap in the demand-and-capacity sharing protocol is the focus on timely demand fulfillment There is a need for collaborative negotiations to uphold timely deliveries with economic efficiency. To address this, the study proposes developing a framework for Collaborative Negotiations in Demand-and-Capacity Sharing to enhance supply network efficiency and prevent opportunistic behavior. Finally, the improved CAP is evaluated through numerical examples and compared to the baseline and the first stage of CAP implementations. Results indicate that incorporating the supply network incentive factor enhances CAP's effectiveness in facilitating collaborative negotiations within supply networks. (c) Copyright 2025 The Authors.

Collaborative automation (CA), which involves humans, AI-based systems, and robots working collaboratively in a shared workspace, is increasingly utilized due to its ability to overcome the limitations of traditional automation methods. Three main subfields of collaborative automation include (1) collaborative robotics, in which cobots are utilized with spatial-visual programming, human-in-the-loop systems, and precision tasks; (2) digital management and decision optimization, which is vital for addressing disruptions; and (3) cyber-augmented systems with augmented reality and artificial intelligence, which leads to more efficient collaborations in cyber-physical systems. This study includes a systematic review and a survey of experts from academia and industry, concluding a six-year NSF-funded project on collaborative automation and AI-based augmented reality tools for future factories and the workforce. The study examines the current purposes, future progress, and benefits of collaborative automation technologies for human agents, particularly in skills sharing, training, and performance. By examining how the collaborative strengths of humans and automation are leveraged jointly, this study aims to unlock and identify future possibilities for efficient, flexible, resilient, and operationally safe cyber-collaborative production systems.

Countless studies have demonstrated the need for improved information sharing; without proper information sharing, the supply network is less competitive, which suggests that participants' trust is low. Both factors indicate that the supply network exhibits low resilience. A novel approach, based on incenting information sharing, and punishing non-sharing, is conceptualized to increase resilience during supply network negotiation. This new model attempts to reduce hidden information and increase the willingness of participants to share private information by weighting the cost contributors' operation costs and desired participants' welfare. This paper establishes an agent-based collaborative negotiation framework with a first simulation. The simulation analyzing the impact of information sharing was conducted for 100 participants. Two scenarios were tested against a baseline: (1) all participants follow a neutral bidding strategy, and (2) a minority of aggressive or conservative bidders are introduced to the simulation. The simulation shows that information sharing is more beneficial to the participants than non-information sharing. In the second part of this research, we expand this work to a case study that implements the framework in a real-world setting: venue parking. A collaboration between the venue and parking lot owner is introduced, including information sharing and designed to streamline the venue visitor's experience. Additionally, it is expected to increase parking revenue and decrease venue costs by reducing overall inefficiencies through aligning incentives.

A necessary prerequisite in establishing agreements within a network of suppliers and customers is a negotiation process. Network parties seek a compromise and agreement about costs and pricing acceptable to each of them, respectively. Price negotiation processes are necessary for supply networks' success; hidden information, however, is a common challenge during such negotiations. A new collaborative negotiation method, not addressed by previous research, is designed to mitigate the impact of the hidden information in supply networks and foster mutual trust among parties. The proposed method incents and motivates the sharing of information, about the supplier's operation costs, by weighting the operations costs and the estimated profit within the auction bids during negotiations. This paper lays the groundwork for creating an agent-based collaborative negotiation protocol. The proposed collaborative negotiation approach results indicate overall savings and significant benefits for the collaborating bidding parties, consequently increasing trust and supply network resilience. The proposed protocol could support decision-makers in establishing a long-lasting, resilient collaborative supply network of benevolent parties. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Precision Agriculture (PA) is a relatively new farming approach, applying science and technology to enhance cost-effectiveness and improve food security by optimizing agricultural practices through the treatment of each crop individually. To support the new practice, an AI-based, responsive monitoring algorithm, called the Dynamic-Adaptive Search algorithm, has been developed to minimize operation costs with the benefit of acquiring new and timely information. Three modules of the algorithm are 1) Module for image processing based on AI, 2) Module for error-responsive search expansion, and 3) Module for estimating stress propagation. Computational experiments have demonstrated that the newly developed algorithm outperforms other alternatives, yielding significantly higher system performance and system gain, compared to other algorithms. The sensitivity analysis confirms the algorithm's ability to deliver within ± 10% of the theoretical optimal value, resulting in economic benefits under varying conditions. The algorithm's applications can be extended to other decision-making situations involving cost-benefit tradeoffs of acquiring more data.

Effective work scheduling for clinical training is essential for medical education, yet it remains challenging. Creating a clinical training schedule is a difficult task, due to the complexity of curriculum requirements, hospital demands, and student well-being. This study proposes the Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling (CCP-AI-MWS) to optimize clinical training schedules. The CCP-AI-MWS integrates the Collaborative Requirement Planning principle with Artificial Intelligence (AI). Two experiments have been conducted comparing CCP-AI-MWS with current practice. Results show that the newly developed protocol outperforms the current method. CCP-AI-MWS achieves a more equitable distribution of assignments, better accommodates student preferences, and reduces unnecessary workload, thus mitigating student burnout and improving satisfaction. Moreover, the CCP-AI-MWS exhibits adaptability to unexpected situations and minimizes disruptions to the current schedule. The findings present the potential of CCP-AI-MWS to transform scheduling practices in medical education, offering an efficient solution that could benefit medical schools worldwide.

The progress in efficient human-robot partnerships has influenced all facets of contemporary industry. A considerable proportion of manufacturing procedures, namely the final assembly and trimming processes, continue to be semi-automated since human agents possess superior skills in performing these tasks. Manufacturers have been progressively integrating augmented reality (AR) into their operations to equip, instruct, direct, and enhance the abilities of their less-experienced staff. As the proficiency of augmented human agents increases, their contact with the robot agents likewise undergoes dynamic modifications. This paper introduces a human-robot collaborative reinforcement learning model (HR-CRL) that enhances the decision-making power of robotic agents. The method considers the changing observable information from the production system environment and the input from human operators. The HR-CRL model is assessed across many scenarios of semi-automated manufacturing activities, which replicate fluctuations in augmented human performance and the environment. The evaluation showcases how robot agents adapt their behavior in response to observable data, enhancing the efficiency of the human-robot collaboration. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)