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Modelling the Problem of Production Scheduling for Reconfigurable Manufacturing Systems

2015· article· en· W749350555 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

VenueProcedia CIRP · 2015
Typearticle
Languageen
FieldEngineering
TopicFlexible and Reconfigurable Manufacturing Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSimulated annealingScheduling (production processes)Job shop schedulingComputer scienceScalabilityFlexible manufacturing systemMathematical optimizationMetaheuristicIndustrial engineeringEngineeringAlgorithmMathematicsEmbedded system

Abstract

fetched live from OpenAlex

Companies are gradually moving towards reconfigurable manufacturing systems (RMS) to achieve both changeable functionality of flexible manufacturing systems (FMS) and scalable capacity of dedicated manufacturing lines (DML) to the extent possible. Despite this expected trend in manufacturing, there is a dearth of literature on scheduling for RMS; papers in the production scheduling literature still mainly focus on either DML or FMS. This paper tackles the problem of scheduling production operations in RMS. After explicitly defining different aspects of the problem, a mathematical model is developed to formally model the problem. Using the model and commercial software of operations research, the small instances of the problem are solved for optimality. To effectively solve large instances of the problem, different simulated annealing metaheuristics are developed. Using numerical experiments, the model and simulated annealing algorithms are evaluated for performance.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.041
GPT teacher head0.220
Teacher spread0.178 · 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