Literature review on using data mining in production planning and scheduling within the context of cyber physical systems

Seeger, Paola Martins; Yahouni, Zakaria; Alpan, Gülgün

doi:10.1016/j.jii.2022.100371

Cited by 13 publications

(8 citation statements)

References 57 publications

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“…In [23] provides a comprehensive review of how data mining is applied to production planning and scheduling, particularly within the context of cyberphysical systems. This review can serve as a valuable resource for researchers and practitioners seeking to implement data-driven approaches in manufacturing and production industries.…”

Section: Introductionmentioning

confidence: 99%

“…[22] Strategic decision support system for urban logistics with a focus on sustainable transport Optimization of urban logistics operations with reduced environmental impacts Integration challenges with existing logistics systems; potential resistance to adopting sustainable transport methods. [23] Comprehensive review of data mining in production planning and scheduling Valuable insights for implementing data-driven approaches in manufacturing within the context of cyberphysical systems Generalization of findings to specific industries may require further research; rapid advancements in technology may impact the relevance of the review over time. [24] Application of logistic regression to predict diabetic foot ulcers Prediction of diabetic foot ulcers with HDL cholesterol as a negative predictor Generalization of findings to diverse diabetic populations; clinical applicability and potential biases in the dataset.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Development of Modern Design Through Data Mining Technology

Meng

2024

IJCAI

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Logistics operations heavily rely on efficient route planning and optimization to ensure the smooth flow of goods and services. To enhance these processes, the simulation of logistics frequent path data mining based on statistical density offers valuable insights. By analyzing vast amounts of historical transportation data, statistical density-based methods can identify frequent paths and patterns in logistics networks. With the data mining process, the statistical density of the logistics in China is computed. The model uses the Fuzzy Associative Monte Carlo (FAMC). The proposed FAMC model estimates the associative rules in the fuzzy model for the computation of the frequent pattern for the estimation of the logistics in the data mining process. Through FAMC model statistical density is computed with an estimation of the logistics path to compute the statistical density model. The logistics path routes are estimated based on the computation of the statistical density for the computation of the data mining-based approach in logistics management. The proposed FAMC model effectively computes the path in the logistics in China with a significant density analysis in a statistical manner.Povzetek: V članku je opisano sodobno oblikovanje z uporabo rudarjenja podatkov, osredotočeno na optimizacijo logističnih poti preko statistične gostote in Fuzzy Associative Monte Carlo modela.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the Development of Modern Design Through Data Mining Technology

Meng

2024

IJCAI

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“…The informatization management of workshop production has historically been a somewhat weak link in the development of manufacturing informatization [1]. In order to improve the level of workshop information management and aid in promoting overall enterprise competitiveness, various workshop production management systems and MES were developed to address the issue of poor information connection between equipment automation systems and ERP, PDM, and other systems.…”

Section: Introductionmentioning

confidence: 99%

Research on Visualization Technology of Production Process for Mechanical Manufacturing Workshop

Li,

Wu,

2023

Applied Sciences

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The visualization of workshop information can affect production management and efficiency. Information can be presented both graphically and non-graphically (for example, in the form of data lists or tables). Graphical representations are intuitive and clear, but currently, most of them are based on statistical data, which makes it difficult to convey logical linkages between information and cannot help managers make decisions effectively. With the aim of designing the workshop production system with visual processes in small-sized enterprises, the key visualization technologies of the process flow chart, including the visual design of process flow chart, process card management, process flow chart release, process control, and production schedule monitoring, were all addressed in detail. On this basis, the mechanical manufacturing workshop production management system was created using C#.NET as the programming language. The main contribution of the research is that the system designed used the process flow chart as the main line through all functional modules and integrated all process data on the process nodes of the process flow chart to realize the graphical monitoring of workshop production schedule. The visualization technology of the process flow chart makes the system simple to use and easy to understand, which significantly improves information management and work efficiency in the workshop. Additionally, it provides the technical foundation for flow-driven production information transfer in the workshop and can serve as a universal standard for the process module in workshop production management systems.

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“…The emerging I4.0 technologies-the Internet of Things (IoT), Cyber-Physical Systems, augmented reality, Artificial Intelligence, Blockchain, cloud computing, Big Data and additive manufacturing (AM)-have provided a new environment for manufacturing to become intelligent and digital [7]. Industry 4.0 defines a production-oriented Cyber-Physical System (CPS) that combines warehousing systems, production facilities, logistics, and even social requirements in order to build global networks of value creation [8]. This is a technique which can turn the traditional machine-based production system into a digital and Artificial Intelligence-based system that allows machines to organize and interact according to the situation, without the need for human-machine interaction (HMI) to control and direct the production flow [9].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

et al. 2022

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With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.

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Literature review on using data mining in production planning and scheduling within the context of cyber physical systems

Cited by 13 publications

References 57 publications

Research on the Development of Modern Design Through Data Mining Technology

Research on the Development of Modern Design Through Data Mining Technology

Research on Visualization Technology of Production Process for Mechanical Manufacturing Workshop

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

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