Visual interactive support for selecting scenarios from time-series ensembles

Schardong, Guilherme; Rodrigues, Ariane M. B.; Barbosa, Simone Diniz Junqueira; Lopes, Hélio

doi:10.1016/j.dss.2018.08.001

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Traditionally, companies engage in stochastic studies by developing deterministic models to support their decision (Karmarkar and Kekre, 1987;Ahmed and Beauchamp-ElMaraghy, 2013;Yang et al, 2018), but involve little variability analysis in their key performance indicators. Authors argue about the use of stochastic behaviour (Schardong et al, 2018) to develop parameters to be used as measurements in the production system. For example, before the use of discrete simulation, performance indicators were calculated via descriptive statistics and then used as indicators to propose changes.…”

Section: Time Variability Management (Tvm)mentioning

confidence: 99%

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

Campos¹,

Ferreira²,

Freires³

2021

BJO&PM

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Goal: This research aims to examine the trade-off behaviour between productivity, quality and maintenance performance and evaluate the efficiency of the framework integrated to a time variability in operational management. Design / Methodology / Approach: The proposed framework integrates a stochastic analysis of process and TVM to manage process variability and analyse the trade-off behaviour between quality, productivity, and maintenance efficiency. The framework generates a control chart for each workstation and analyses the operator's time to execute an assembly process for six workstations. The processes are characterised and evaluated via stochastic simulation, resulting in a trade-off analysis with a multicriteria approach. Results: A relational graphic of performance dimensions shows that productivity and maintenance performance have a high dependency on time variability management, whereas quality has a minor impact over trade-off decisions. The proposed framework has a huge significance to operational management which allow to apply complex models to time variability analysis and organisation performance. Limitations of the investigation: The trade-off analysis in this study is limited to a constant trade-off model to validate the framework. Practical implications: This framework, used in association with traditional management philosophies, is a powerful tool that permits managers to develop a trade-off analysis and make decisions on workstations. Thus, a research field that correlates time variability with performance dimensions may be validated by this framework. Originality / Value: A framework that allows the use of MCDM to analyse the trade-off behaviour on organization performance and a trade-off analysis between performance dimensions and time variability management in operational performance.

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Section: Time Variability Management (Tvm)mentioning

confidence: 99%

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

Campos¹,

Ferreira²,

Freires³

2021

BJO&PM

View full text Add to dashboard Cite

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“…company sales data, stock prices, and industrial data. Many researches related to analyzing time series have been carried out, such as: prediction [1], [2], classification [3], clustering [4], anomaly detection [5], visualization [6], pattern recognition [7] and trend analysis [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Similarity Measurement and Clustering Framework for Time Series Based on Convolution Neural Networks

et al. 2020

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In recent years, with the development of machine learning, especially after the rise of deep learning, time series clustering has been proven to effectively provide useful information in cloud computing and big data. However, many modern clustering algorithms are difficult to mine the complex features of time series, which is important for further analysis. Convolutional neural network provides powerful feature extraction capabilities and has excellent performance in classification tasks, but it is hard to be applied to clustering. Therefore, a similarity measurement method based on convolutional neural networks is proposed. This algorithm converts the number of output changes of the convolutional neural network in the same direction into the similarity of time series, so that the convolutional neural network can mine unlabeled data features in the clustering process. Especially by preferentially collecting a small amount of high similarity data to create labels, a classification algorithm based on the convolutional neural network can be used to assist clustering. The effectiveness of the proposed algorithm is proved by extensive experiments on the UCR time series datasets, and the experimental results show that its superior performance than other leading methods. Compared with other clustering algorithms based on deep networks, the proposed algorithm can output intermediate variables, and visually explain the principle of the algorithm. The application of financial stock linkage analysis provides an auxiliary mechanism for investment decision-making. INDEX TERMS time series, clustering, convolutional neural networks, similarity measurement

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