Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

Bi, Mingjie; Kovalenko, Ilya; Tilbury, Dawn M.; Barton, Kira

doi:10.1080/00207543.2023.2200567

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…Agents that have a mixture of those strategies are common. Agents within automated manufacturing strive to change their surroundings to fulfill their goals based on real-time data directly from sensors and information communicated from other agents [15]. Goals in manufacturing are connected to actions or processes, one goal could be to have soft edges and the process could be grinding.…”

Section: Background Of Plug and Produce And Configurable Multi-agent ...mentioning

confidence: 99%

A Generic Structure for the Integration of Customized Industrial Gantry Robots into Agents of Plug & Produce Automated Manufacturing System

Nilsson,

Danielsson

2024

Advances in Transdisciplinary Engineering

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The demand for customized products in a saturated market of trendy customers forces the manufacturing industries to transform their manufacturing from a high volume of uniformed products to low volumes and a high mix of products. High mix and low volume manufacturing is most often manually performed since existing automation solutions are only profitable for mass manufacturing, due to explicitly designed control software where the product data is implemented as low-level control code. Highly flexible automated manufacturing systems such as Plug & Produce are requested, but challenges still exist before industrial implementation. This article proposes a digitally configurable system where data for new or modified products data is configured from the perspective of the product and its manufacturing processes instead of the manufacturing resources. In a Plug & Produce system, process modules with manufacturing resources are easy to replace for new or modified products and possibly to duplicate if higher capacity is needed. Configurable multi-agent systems are proposed by several researchers as a control system for Plug & Produce. An agent is a piece of autonomous computer code that negotiates with other agents and concurrently solves tasks, distributed on parts and resources. A part is a part of a product and part agents handle manufacturing goals for the parts. Resource agents know their capability and start operating as soon as they are plugged in. Resource agents follow pluggable process modules containing manufacturing resources and act as drivers for the modules. Gantry robots have by design a naturally orthogonal coordinate system and most often lack the functionality to handle work and tool coordinate objects as standard industrial robots do. Work objects refer to a base coordinate system and tool objects contain a reference to the tool center point. These references are in this article integrated into resource agents together. A place coordinate agent has the global perspective of the Plug & Produce cell and provides the process modules with reference coordinates of the place they are plugged into. Coordinates are recalculated from a product perspective into a resource perspective by coordinate transformations built into the skills of resource agents. This structure enables the possibility for process planners in the manufacturing company to make changes on a daily basis. A test with a gantry robot Plug & Produce demonstrator was performed and presented in this article to verify the generic structure of the gantry robot control system into agents.

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Section: Background Of Plug and Produce And Configurable Multi-agent ...mentioning

confidence: 99%

A Generic Structure for the Integration of Customized Industrial Gantry Robots into Agents of Plug & Produce Automated Manufacturing System

Nilsson,

Danielsson

2024

Advances in Transdisciplinary Engineering

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“…Additionally, brands need to be transparent about the use of AI to avoid misleading consumers .The future of AI in marketing personalization holds exciting possibilities. As AI models evolve and incorporate new data sources, brands can create hyperpersonalized experiences that further blur the lines between marketing and genuine customer connection (Bi et al, 2024). In conclusion, AI-powered marketing personalization represents a significant shift.…”

Section: Theoretical Framework and Consumer Engagement Theoriesmentioning

confidence: 99%

The Role of Ai in Marketing Personalization: A Theoretical Exploration of Consumer Engagement Strategies

Sodiq Odetunde Babatunde,

Opeyemi Abayomi Odejide,

Tolulope Esther Edunjobi

et al. 2024

Int. j. manag. entrep. res

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This paper explores the transformative potential of Artificial Intelligence (AI) in personalizing marketing strategies. It delves into the theoretical underpinnings of consumer engagement sand investigates how AI can be leveraged to develop targeted and relevant marketing experiences. AI can personalize messages based on consumer behavior and demographics, influencing the processing route and maximizing engagement. This theory explores the use of game mechanics to motivate and engage users. AI can personalize gamified marketing experiences, tailoring rewards and challenges to individual consumer preferences, driving deeper engagement. Algorithms can analyze vast amounts of customer data to predict individual preferences and behaviors. This allows for targeted advertising, product recommendations, and content that resonates with specific consumer segments. Natural Language Processing (NLP), AI-powered NLP tools analyze customer reviews, social media conversations, and other forms of unstructured data. This allows brands to understand customer sentiment and personalize communication styles for optimal engagement AI-powered chatbots and virtual assistants can provide personalized customer support and product recommendations in real-time, fostering a more interactive and engaging brand experience. Potential Benefits and Considerations Personalized marketing messages and experiences cater to individual needs and preferences, leading to higher satisfaction and loyalty. By tailoring content and offerings to specific consumer segments, brands can establish a more relevant and relatable image. Improved Conversion Rates, Personalized marketing campaigns can be highly targeted and effective, leading to increased conversions and sales. Balancing personalization with data privacy concerns is crucial. Transparency and user control over data collection practices are essential. AI algorithms can perpetuate biases present in training data. Ensuring fairness and inclusivity in AI-powered marketing is paramount. AI is revolutionizing marketing personalization. By leveraging AI's analytical capabilities and understanding the theoretical aspects of consumer engagement, brands can develop targeted and relevant marketing strategies that foster deeper customer connections and drive business growth. Keywords: AI Personalization, Consumer Engagement, Marketing Strategy, Theoretical Exploration, Data Privacy, Algorithmic Bias.

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Real-time rescheduling for smart shop floors: an integrated method

Sun,

Liu,

Zhang

et al. 2024

Flex Serv Manuf J

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Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

Cited by 7 publications

References 43 publications

A Generic Structure for the Integration of Customized Industrial Gantry Robots into Agents of Plug & Produce Automated Manufacturing System

A Generic Structure for the Integration of Customized Industrial Gantry Robots into Agents of Plug & Produce Automated Manufacturing System

The Role of Ai in Marketing Personalization: A Theoretical Exploration of Consumer Engagement Strategies

Real-time rescheduling for smart shop floors: an integrated method

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