Production data analytics for production scheduling

Li, Q.Y.; Wang, L.; Xu, Jinyi

doi:10.1109/ieem.2015.7385838

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Feng et al (2020) for example used process variables and sensors to develop an energy consumption optimized scheduling of flexible workshops. Li et al (2015) applied production data to improve the efficiency of a shop floor. Jiang and Zhang (2019) developed an algorithm which is able to provide an energy consumption optimized scheduling for hybrid flow shops with limited buffers.…”

Section: Resultsmentioning

confidence: 99%

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Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

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Advanced analytics applications in smart manufacturing provide beneficiary effects such as predictive maintenance, energy optimized factories, and automized quality management of products. While the benefits and thus the sustainability effects are well investigated, the research field is characterized by many single applications with little conceptual synthesis. Therefore, this research aims to systematize the field and to identify specific sustainability themes in context of advance analytics applications. This chapter provides an overview of existing advanced analytics applications in smart manufacturing and clusters the addressed sustainability themes. Applying a systematic literature review, following the PRISMA statement, 65 different applications within four research perspective could be identified. Furthermore, ten different sustainability-related effects of the applications in smart manufacturing unfold. The content analysis resulted in 27 sustainability themes, which holistically cover all spheres of sustainability, with a focus on the social and economic spheres.

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Section: Resultsmentioning

confidence: 99%

“…Qu et al (2016) also argued that expert knowledge about the manufacturing system is needed to design effective algorithms. Li et al (2015) praised the experience which comes from many working years. Expert knowledge impacts the social pillar because it demands a corresponding expertise from developers.…”

Section: Performancementioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

View full text Add to dashboard Cite

show abstract

“…In the field of natural sciences, the Pearson correlation coefficient is widely used to measure the degree of correlation between two variables. The Pearson correlation coefficient between two variables is defined as the quotient of the covariance and standard deviation between the two variables, as shown in Equation (2), where cov(X,Y) represents the covariance between X and Y, δX represents the standard deviation of X, and E[X] represents the expected value of X.…”

Section: Input Variable Selectionmentioning

confidence: 99%

“…With the continuous progress of production and the accumulation of man-hour deviations, the production plan executed on the production line deviates from the pre-arranged production plan. This estimation of production tasks and operation time based on inaccurate manhours parameters can lead to significant deviations between the plan and actual production, which can easily lead to implementation gaps [1,2]. Even through rescheduling and resource rearrangement, it is difficult to compensate for the impact of man-hour deviations.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Man-Hour Prediction in Structural Steel Fabrication

Wei,

Li,

Niu

et al. 2024

Processes

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Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low. In this study, we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication. The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques, Pearson correlation coefficient for feature selection, and the Random Forest Regression (RFR) for prediction. Compared with the other three Machine-Learning (ML) algorithms, the Random Forest algorithm has the best performance. The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy so it is suitable for man-hours prediction.

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“…With the continuous progress of production and the accumulation of manhour deviations, the production plan executed on the production line deviates from the pre-arranged production plan. This estimation of production tasks and operation time based on inaccurate manhours parameters can lead to significant deviations between plan and actual production, which can easily lead to implementation gaps [1,2].Even through rescheduling and resource rearrangement, it is difficult to compensate for the impact of man-hour deviations. Moreover, rescheduling and resource rearrangement consume a lot of manpower and time, thereby reducing the feasibility of the entire production plan, which makes it difficult for production plans to effectively guide the actual production operations of enterprises.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Man-Hour Prediction in Structural Steel Fabrication

Wei,

Li,

Niu

et al. 2024

Preprint

View full text Add to dashboard Cite

Longitudinal cutting is a most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules.However, currently the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low.In this study,we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication.The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques,Pearson correlation coefficient for feature selection,and the Random Forest Regression(RFR) for prediction.Compared with the other three Machine Learning(ML) algorithms, the Random Forest algorithm has the best performance.The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy, so that it is suitable for man-hours prediction.

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Production data analytics for production scheduling

Cited by 5 publications

References 4 publications

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

A Study on the Man-Hour Prediction in Structural Steel Fabrication

A Study on the Man-Hour Prediction in Structural Steel Fabrication

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