Online task distribution simulation in smart factories

Tsourma, Maria; Zikos, Stylianos; Drosou, Anastasios; Tzovaras, Dimitrios

doi:10.1109/sims.2018.8355301

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Other authors (Ansari, Glawar, & Nemeth 2019;Brik, Bettayeb, Sahnoun, & Duval 2019;Fu, Ding, Wang, & Wang 2018;H. Li 2016;Qu, Wang, Govil, & Leckie 2016;Tsourma, Zikos, Drosou, & Tzovaras 2018;Uriarte, Ng, & Moris 2018), employed other optimization techniques, such as (Tsourma et al 2018) that proposed a Task Distribution Engine to automate and optimize the task scheduling and resources assignment procedure in industrial environments. We also found studies that performed Prescriptive Analytics by using predictive models to directly perform actions: Deep Learning (Richter, Streitferdt, & Rozova 2017); Regression Trees and Nearest Neighbors (Romeo, Paolanti, Bocchini, Loncarski, & Frontoni 2018); and SVM combined with Q-Learning (Qu et al 2016).…”

Section: Descriptive Analyticsmentioning

confidence: 99%

“…In particular, the most explored method was the Genetic Algorithm (Khayyam et al, 2019; Silva et al, 2020). Other authors (Ansari et al, 2019; Brik et al, 2019; Fu et al, 2018; H. Li, 2016; Qu et al, 2016; Tsourma et al, 2018; Uriarte et al, 2018), employed other optimization techniques, such as (Tsourma et al, 2018) that proposed a Task Distribution Engine to automate and optimize the task scheduling and resources assignment procedure in industrial environments. We also found studies that performed Prescriptive Analytics by using predictive models to directly perform actions: Deep Learning (Richter et al, 2017); Regression Trees and Nearest Neighbors (Romeo et al, 2018); and SVM combined with Q‐Learning (Qu et al, 2016).…”

Section: Literature Review Analysismentioning

confidence: 99%

See 1 more Smart Citation

Business analytics in Industry 4.0: A systematic review

Silva

Cortez

Pereira³

et al. 2021

Expert Systems

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Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modeling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on this Business Analytics usage, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analyzed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modeling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.

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Section: Descriptive Analyticsmentioning

confidence: 99%

Section: Literature Review Analysismentioning

confidence: 99%

Business analytics in Industry 4.0: A systematic review

Silva

Cortez

Pereira³

et al. 2021

Expert Systems

View full text Add to dashboard Cite

show abstract

Due Date-Related Order Prioritization for Scheduling with Decision Support in Dynamic Environments

Bojko

Franke

Pelliccia

et al. 2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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Personaleinsatzplanung in der Logistik

Haid

Unruh

Pröger

et al. 2021

Zeitschrift Für Wirtschaftlichen Fabrikbetrieb

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Die Digitalisierung führt zu vielfältigen Veränderungen in der Logistik. Immer mehr Daten stehen zur Verfügung, die detaillierte Prognosen über Maschinen oder Bedarfe ermöglichen. Mitarbeiterdaten erlauben es Unternehmen, prozessuralen Mehrwert durch die Bewegungs- oder Leistungsanalyse des Arbeitstages zu erreichen. Dies kann aber für die Mitarbeiter nachteilige Konsequenzen haben. In diesem Beitrag wird ein Ansatz vorgestellt, der dem entgegenwirkt, indem die Mitarbeiterpräferenzen in die Personaleinsatzplanung einbezogen werden. In der menschzentrierten, d. h. am Wohlbefinden der Mitarbeiter ausgerichteten, Entwicklung des Systems werden insbesondere ethische Bedenken frühzeitig aufgenommen und berücksichtigt. Fairness, Mitarbeiterzufriedenheit, Autonomie und der Schutz der Privatsphäre der Mitarbeiter sind Leitsätze, die die Gestaltung des Systems bestimmen.

show abstract

Online task distribution simulation in smart factories

Cited by 3 publications

References 12 publications

Business analytics in Industry 4.0: A systematic review

Business analytics in Industry 4.0: A systematic review

Due Date-Related Order Prioritization for Scheduling with Decision Support in Dynamic Environments

Personaleinsatzplanung in der Logistik

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