Smart digital twin for ZDM-based job-shop scheduling

[EN] The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies¿ competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.The research that led to these results received funding from the European Union H2020 Programme with grant agreement No. 825631 Zero-Defect Manufacturing Platform (ZDMP) and Grant agreement No. 958205 Industrial Data Services for Quality Control in Smart Manufacturing (i4Q), and from the Spanish Ministry of Science, Innovation and Universities with Grant Agreement RTI2018-101344-B-I00 "Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)"Serrano Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart digital twin for ZDM-based job-shop scheduling. IEEE. 510-515. https://doi.org/10.1109/MetroInd4.0IoT51437.2021.948847351051

Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective

[EN] Based on a scientific literature review in the conceptual domain defined by smart manufacturing scheduling (SMS), this article identifies the benefits and limitations of the reviewed contributions, establishes and discusses a set of criteria with which to collect and structure its main synergistic attributes, and devises a conceptual framework that models SMS around three axes: a semantic ontology context, a hierarchical agent structure, and the deep reinforcement learning (DRL) method. The main purpose of such a modelling research is to establish a conceptual and structured relationship framework to improve the efficiency of the job shop scheduling process using the approach defined by SMS. The presented model orients the job shop scheduling process towards greater flexibility, through enhanced rescheduling capability, and towards autonomous operation, mainly supported by the use of machine learning technology. To the best of our knowledge, there are no other similar conceptual models in the literature that synergistically combine the potential of the specific set of Industry 4.0 principles and technologies that model SMS. This research can provide guidance for practitioners and researchers¿ efforts to move toward the digital transformation of job shops.The research leading to these results received funding from the European Union H2020 Programme ,Belgium with grant agreements No. 825631 "Zero-Defect Manufacturing Platform (ZDMP) ", No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q) " and 872548 "Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs (DIH4CPS) ", from Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe" and the Regional Department of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana entitled "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065).Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2022). Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective. Journal of Manufacturing Systems. 63:185-202. https://doi.org/10.1016/j.jmsy.2022.03.0111852026