Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Ranade, Akshay; Gómez, Javier; de Juan, Andrew; Chicaiza, William D.; Ahern, Michael; Escaño, Juan M.; Hryshchenko, Andriy; Casey, Olan; Cloonan, Aidan; O’Sullivan, Dominic; Bruton, Ken; McGibney, Alan

doi:10.3390/en17081818

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…With the vigorous development of manufacturing technology and artificial intelligence technology, intelligent manufacturing has become one of the mainstream directions of the manufacturing industry [1,2]. So far, artificial intelligence technology has been effectively applied in many directions and fields of the manufacturing industry.…”

Section: Introductionmentioning

confidence: 99%

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Xu,

Fei

et al. 2024

Mathematics

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Considering the requirements of the actual production scheduling process, the utilization of the genetic programming hyper-heuristic (GPHH) approach to automatically design dispatching rules (DRs) has recently emerged as a popular optimization approach. However, the decision objects and decision environments for routing and sequencing decisions are different in the dynamic flexible job shop scheduling problem (DFJSSP), leading to different required feature information. Traditional algorithms that allow these two types of scheduling decisions to share one common feature set are not conducive to the further optimization of the evolved DRs, but instead introduce redundant and unnecessary search attempts for algorithm optimization. To address this, some related studies have focused on customizing the feature sets for both routing and sequencing decisions through feature selection when solving single-objective problems. While being effective in reducing the search space, the selected feature sets also diminish the diversity of the obtained DRs, ultimately impacting the optimization performance. Consequently, this paper proposes an improved GPHH with dual feature weight sets for the multi-objective energy-efficient DFJSSP, which includes two novel feature weight measures and one novel hybrid population adjustment strategy. Instead of selecting suitable features, the proposed algorithm assigns appropriate weights to the features based on their multi-objective contribution, which could provide directional guidance to the GPHH while ensuring the search space. Experimental results demonstrate that, compared to existing studies, the proposed algorithm can significantly enhance the optimization performance and interpretability of energy-efficient DRs.

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Section: Introductionmentioning

confidence: 99%

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Xu,

Fei

et al. 2024

Mathematics

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Theoretical analysis and technical application of mechanical intelligent manufacturing based on system digital-driven technology

Li,

Qin,

Wang

2024

J. Eng. Appl. Sci.

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With the accelerating process of global industrialization, the automated fabrication industry, as an important part of the industry, is also developing rapidly. Due to the low efficiency and high resource consumption of traditional manufacturing, it is more important to improve the production efficiency of conventional manufacturing and realize the resource-saving development mode. Due to the characteristics of system digital-driven technology, it can promote the intelligent development of the manufacturing industry. Therefore, the deep integration of system digital-driven technology and manufacturing is conducive to the intelligent promotion of manufacturing. Comparing the difference between the traditional manufacturing industry and the intelligent manufacturing industry, the application potential of system digitalization technology in the manufacturing field is analyzed. Based on the analysis of the theoretical basis of the system digital-driven technology and the application of the system digital technology in the main fields, the importance of the technology for the improvement of the accuracy index in the manufacturing process is emphatically analyzed. The maximum increase can be 5.3%, and the minimum increase can be 3.3%. It can be seen that the deep integration of digital-driven technology and manufacturing can not only improve the intelligent level of manufacturing, realize the intensive development of the industry, but also realize the data sharing of the entire industrial chain.

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Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Cited by 2 publications

References 29 publications

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Theoretical analysis and technical application of mechanical intelligent manufacturing based on system digital-driven technology

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