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MULTI-AGENT SYSTEM MODEL FOR DYNAMIC SCHEDULING IN FLEXIBILE JOB SHOP SUBJECT TO RANDOM MACHINE BREAKDOWN

2022· article· en· W4317815167 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

Venue2022 Winter Simulation Conference (WSC) · 2022
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceScheduling (production processes)Job shop schedulingDynamic priority schedulingSubject (documents)Mathematical optimizationEmbedded systemMathematicsWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

This paper presents a model for dynamic scheduling in a smart manufacturing system that can be used in a manufacturing environment subject to random machine breakdown. We employ a multi-agent system (MAS) to schedule work on a system of machines in real-time. We propose that such a system should be less sensitive to unforeseen disruptions to the system whilst yielding good results with respect to the total flowtime for parts requested of the system. The approach employed is a completely reactive approach, and as such has the benefit of not requiring the determination of a nominal schedule. Rather, we take advantage of self-organizing nature of the MAS to guide work scheduling. To evaluate the efficacy of our proposed model, we compare its performance to that of a system using predictive-reactive scheduling to solve a furniture manufacturing problem.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score1.000

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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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