A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines

Subramaniyan, Mukund; Skoogh, Anders; Salomonsson, Hans

doi:10.1016/j.cie.2018.04.024

Cited by 86 publications

(29 citation statements)

References 24 publications

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“…We identified six basic steps that should be included in ML model building, and they are also similar for other research applications (Kiyohara et al, 2018(Kiyohara et al, , 2018Kontokosta and Tull, 2017;Subramaniyan et al, 2018). However, different validation measures can be introduced in both the training process and the simulation process.…”

Section: Discussionmentioning

confidence: 99%

Using SHAP to interpret XGBoost predictions of grassland degradation in Xilingol, China

Batunacun

Wieland

Lakes

et al. 2020

Preprint

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Abstract. Machine learning (ML) and data-driven approaches are increasingly used in many research areas. XGBoost is a tree boosting method that has evolved into a state-of-the-art approach for many ML challenges. However, it has rarely been used in simulations of land use change so far. Xilingol, a typical region for research on serious grassland degradation and its drivers, was selected as a case study to test whether XGBoost can provide alternative insights that conventional land-use models are unable to generate. A set of twenty drivers was analysed using XGBoost, involving four alternative sampling strategies, and SHAP to interpret the results of the purely data-driven approach. The results indicated that, with three of the sampling strategies (over-balanced, balanced and imbalanced), XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation. Four drivers accounted for 99 % of the grassland degradation dynamics in Xilingol. These four drivers were spatially allocated, and a risk map of further degradation was produced. The limitations of using XGBoost to predict future land-use change are discussed.

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Section: Discussionmentioning

confidence: 99%

Using SHAP to interpret XGBoost predictions of grassland degradation in Xilingol, China

Batunacun

Wieland

Lakes

et al. 2020

Preprint

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“…Wedel et al analyzed bottleneck detection methods, such as analytical or simulation-based, operator-knowledge-based, and buffer-levelbased methods, and proposed new short-term, real-time, and future bottleneck detection methods [31]. More recently, to recognize bottlenecks in a manufacturing system, algorithms have been developed that detect bottlenecks in machine data through the machine-learning-based hierarchical clustering of unsupervised learning by applying an improved aggregation method or through a statistical framework [32][33][34][35].…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Jung

Park²,

Lee³

et al. 2021

Journal of Computing and Information Science in Engineering

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Implementing digital transformation in the garment industry is very difficult, owing to its labor-intensive structural characteristics. Further, the productivity of a garment production system is considerably influenced by a combination of processes and operators. This study proposes a simulation-based hybrid optimization method to maximize the productivity of a garment production line. The simulation reflects the actual site characteristics, i.e., process and operator level indices, and the optimization process reflects constraints based on expert knowledge. The optimization process derives an optimal operator sequence through a genetic algorithm, and sequentially removes bottlenecks through workload analysis based on the results. The proposed simulation optimization method improved productivity by ∼67.4%, which is 52.3% higher than that obtained by the existing meta-heuristic algorithm. The correlation between workload and production was verified by analyzing the workload change trends. This study holds significance because it presents a new simulation-based optimization model that further applies the workload distribution method by eliminating bottlenecks and digitizing garment production lines.

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“…In contrast, Subramaniyan, Skoogh, Salomonsson, Bangalore and Bokrantz developed an algorithm for predicting bottlenecks on production lines in the automotive industry. They combined the ARIMA methodology with a real-databased technique, making it easier for engineers to manage bottlenecks and achieve higher bandwidth (Subramaniyan et al, 2018). Lai, Shui and Ni used a Two-Layer Long Short-Term Memory (LSTM) to predict bottlenecks on the chassis assembly line.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of forecasting methods in production engineering

Winkowski

2019

Engineering Management in Production and Services

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Business management is a continuous decision-making process. It is difficult to imagine a company that does not use forecasting techniques. Even small enterprises without relevant forecasting departments more or less consciously anticipate future events, forecasting the volume of production and setting directions for development. Today’s production companies must quickly adapt to changing customer requirements, implementing structural and technological changes and delivering projects related to the production of new products. Under the dynamically changing conditions, the functioning and effective management of modern enterprises depend on future-oriented information. This increases the validity of forecasting. This article aimed to identify forecasting methods and areas of their use in production engineering. The publications on this subject were reviewed in the Scopus database, using the time frame from January 1970 to June 2018. An original classification of research subareas was created using VOS viewer software, and then, a bibliometric map was developed to visualise the results of the word coexistence analysis. The analysis of the co-occurrence and co-classification of words made it possible to indicate research subareas of forecasting in production engineering and related emerging research areas and issues.

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A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines

Cited by 86 publications

References 24 publications

Using SHAP to interpret XGBoost predictions of grassland degradation in Xilingol, China

Using SHAP to interpret XGBoost predictions of grassland degradation in Xilingol, China

Simulation-Based Hybrid Optimization Method for the Digital Twin of Garment Production Lines

Classification of forecasting methods in production engineering

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