An integrated data- and capability-driven approach to the reconfiguration of agent-based production systems

Abstract

Industry 4.0 promotes highly automated mechanisms for setting up and operating flexible manufacturing systems, using distributed control and data-driven machine intelligence. This paper presents an approach to reconfiguring distributed production systems based on complex product requirements, combining the capabilities of the available production resources. A method for both checking the “realisability” of a product by matching required operations and capabilities, and adapting resources is introduced. The reconfiguration is handled by a multi-agent system, which reflects the distributed nature of the production system and provides an intelligent interface to the user. This is all integrated with a self-adaptation technique for learning how to improve the performance of the production system as part of a reconfiguration. This technique is based on a machine learning algorithm that generalises from past experience on adjustments. The mechanisms of the proposed approach have been evaluated on a distributed robotic manufacturing system, demonstrating their efficacy. Nevertheless, the approach is general and it can be applied to other scenarios.

Understanding the role of demand and supply integration in achieving retail supply chain agility: An information technology capability perspective

“…We provide a contextual interpretation to reveal IT as a processing capability underpinning of DSI, helping to capture the dynamism of supply chain agility. Second, IT capability‐driven is an effective solution to the paradox of “information technology‐operational agility” (Li & Chan, 2019; Scrimieri et al, 2023; Zhou et al, 2018). This study contends that IT, as a strategic delivery capability, is essentially the processing function of information resources, and activates the co‐creation behaviors in supply chain.…”

RRPDG: A Graph Model to Enable AI-Based Production Reconfiguration and Optimization

“…A second kind of approach formalizes the machine's functionalities, often relying on ontologies [14], [15], [16]. Most models formalize the machine's atomic functionalities (i.e., services) through a set of capabilities and constraints [17] describing their effects on the environment.…”

“…While these methodologies perform well in their low-level vision of the production processes, they are not suitable for addressing higher-level process optimization due to their fixed structure. This limitation is mitigated in [15] by considering a production process as a multiagent system. However, the proposed approach is limited to "plug and produce" production plants, which are still not the state of the practice in manufacturing.…”