An agent approach to flexible automated production systems based on discrete and continuous reasoning

Rehberger, Sebastian; Spreiter, Lucas; Vogel‐Heuser, Birgit

doi:10.1109/coase.2016.7743550

“…PAs obtain and update A nm , b nm after querying RAs and obtaining the nominal cost parameters for each state. While the methods to develop and update the necessary RA models are out of the scope of this paper, we envision that existing modeling frameworks, e.g., [14], [60], can be extended to calculate A nm , b nm . For the soft constraint violation cost, the agent associated with each soft constraint, ag sc , provides the a k s f t , b k s f t to the DA-PA. a k s f t , b k s f t represent how undesirable the constraint violation would be to ag sc , in terms of a performance metric k. Currently, the cost of constraint violations is determined using feedback from an operator or a subject matter expert.…”

Section: ) State Costsmentioning

confidence: 99%

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Kovalenko

¹

,

Balta²,

Tilbury

³

et al. 2023

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.Note to Practitioners-An intelligent product is a product in a manufacturing system that is able to make decisions based on a set of specifications and affect its own production process. Intelligent products have often been proposed to address the challenges associated with small-batch manufacturing and highly customized production. Specifically, by using intelligent products, manufacturers would be able to complete small orders without the need to reconfigure or reschedule operations in the manufacturing system. However, one of the major challenges in the implementation of this control strategy is the need to develop methods that allow intelligent products to cooperate with Manuscript

show abstract

“…ADACOR provides a formal specification to describe the behavior of its proposed agents using Petri nets [62], [63]. In [21], [70], the internal architectures that enable agent communication and decisionmaking of two types of manufacturing agents -product agent and resource agent -are developed. However, these architectures do not provide well-defined agent capabilities, and thus cannot support quantifiable implemented learning or the communication of these learnings (R1 and R2).…”

Section: B Distributed Control Approachesmentioning

confidence: 99%

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Kovalenko

¹

,

Moyne

²

,

Bi

³

et al. 2022

View full text Add to dashboard Cite

Manufacturers are constantly looking to enhance the performance of their manufacturing systems by improving their ability to address disruptions and disturbances, while reducing cost and maximizing quantity and quality. Even though innovative mechanisms for adaptability and flexibility continuously contribute to the smart manufacturing evolutionary process, they generally stop short of providing a capability for coordinated on-line learning. This is especially true when that learning requires exploration outside of established operational boundaries or uses artificial intelligence (as opposed to purely human intelligence) as part of the dynamic implementation of learning. In this work, we provide a vision for the development of an automated learning control architecture to extend the adaptability and flexibility capabilities of manufacturing systems. As part of this vision, we describe a set of requirements and objectives that, if addressed, provide an environment to allow distributed and automated learning across the manufacturing ecosystem. We then provide an example communication and control architecture that meets these requirements and objectives by gathering information, building a dynamic knowledge base, distributing intelligence, making decisions, and adapting the control commands sent to the equipment and people across the manufacturing ecosystem. The example architecture leverages both centralized and distributed control strategies and the ability to switch between the strategies to gather and learn from information in the system. Example case studies are provided illustrating how this architecture can be used to improve manufacturing system performance.

show abstract

“…PAs obtain and update A nm , b nm after querying RAs and obtaining the nominal cost parameters for each state. While the methods to develop and update the necessary RA models are out of the scope of this paper, we envision that existing modeling frameworks, e.g., [14], [60], can be extended to calculate A nm , b nm . For the soft constraint violation cost, the agent associated with each soft constraint, ag sc , provides the a k sf t , b k sf t to the DA-PA. a k sf t , b k sf t represent how undesirable the constraint violation would be to ag sc , in terms of a performance metric k. Currently, the cost of constraint violations is determined using feedback from an operator or a subject matter expert.…”

Section: ) Agent Association Functionmentioning

confidence: 99%

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Kovalenko¹,

Balta²,

Tilbury³

et al. 2021

Preprint

View full text Add to dashboard Cite

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.

show abstract

An agent approach to flexible automated production systems based on discrete and continuous reasoning

Cited by 15 publications

References 21 publications

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Contact Info

Product

Resources

About