A generic hierarchical clustering approach for detecting bottlenecks in manufacturing

Subramaniyan, Mukund; Skoogh, Anders; Muhammad, Azam Sheikh; Johansson, Björn; Roser, Christoph

doi:10.1016/j.jmsy.2020.02.011

Cited by 50 publications

(16 citation statements)

References 27 publications

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“…They used the k-means clustering and topic modelling techniques to build a cluster of supplier capabilities topics. Subramaniyan et al ( 2020 ) clustered time-series bottle-neck data using dynamic time wrapping and complete-linkage agglomerative hierarchical clustering technique for determining bottlenecks in manufacturing systems. Ahn and Chang ( 2019 ) discussed business process management for manufacturing models.…”

Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

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Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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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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“…The classification models based on hierarchical decision structures are attracting significant research attention in the recent years. This is because they have demonstrated an appreciable predictive performance on a wide variety of interesting engineering applications like text classification [25] , intrusion detection [26] , manufacturing [27] and credit scoring prediction [28] . Biomedical applications like generation of molecular graphs [29] , lung nodule malignancy classification [30] , COVID-19 detection [31] , skin lesion classification [32] and detection of Alzheimer’s disease [33] have also incorporated the use of hierarchical learning methods to build efficient classifiers.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Agarwal

Covarrubias‐Zambrano

Bossmann

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Objective: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. Methods and procedures: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. Results: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. Conclusion: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. Clinical impact: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

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A generic hierarchical clustering approach for detecting bottlenecks in manufacturing

Cited by 50 publications

References 27 publications

Data clustering: application and trends

Data clustering: application and trends

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

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

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