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Real-Time Optimization of Maintenance and Production Scheduling for an Industry 4.0-Based Manufacturing System

2020· article· en· W3046693044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIndustry 4.0Manufacturing engineeringCyber-physical systemAdvanced manufacturingManufacturing execution systemManufacturingScheduling (production processes)Integrated Computer-Aided ManufacturingProcess development execution systemComputer scienceLead timeComputer-integrated manufacturingEngineeringBusinessOperations managementMarketing

Abstract

fetched live from OpenAlex

The adaption of state-of-the-art inventions in information technology and industrial informatics in manufacturing has led to the advent of Industry 4.0, commonly known as the fourth industrial revolution. Industry 4.0 will take manufacturing productivity and quality to new levels and create enormous opportunities for business and revenue growth. Unlike classical manufacturing systems, Industry 4.0-based manufacturing systems are supported by several advanced technologies (known as Industry 4.0 concepts), which include cyber-physical systems (CPS) and internet of things (IoT), among other Industry 4.0 concepts. The adoption of such technology (i.e., Industry 4.0) allows for the delivery of real-time actionable data for smart decision-making. In order to fully realize the potential of such technologies, real-time decision making should be present in all aspects of the manufacturing process. This includes two core components of manufacturing: maintenance and production scheduling.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.346
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.220
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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