An analytic hierarchy process augmented with expert rules for product driven control in cyber-physical manufacturing systems

Attajer, Ali; Darmoul, Saber; Chaabane, Sondès; Sallez, Yves; Riane, Fouad

doi:10.1016/j.compind.2022.103742

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…To adapt to current conditions, a Reinforcement learning mechanism was developed which updates flow of material based on real time sensor data and other monitoring devices (Kumar et al, 2020). Another control mechanism that was implemented involves an Analytic Hierarchy Process with expert rules applied to a dispatching problem in an assembly process to adapt to disturbances in production (Attajer et al, 2022). Specific external disruptions could include shifts in the market, prompting the development of a machine learning context-aware manufacturing system to effectively respond to varying demands (Ye et al, 2022).…”

Section: Previous Workmentioning

confidence: 99%

Cognitive manufacturing: definition and current trends

El Kalach,

Yousif,

Wuest

et al. 2024

J Intell Manuf

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Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

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Section: Previous Workmentioning

confidence: 99%