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Continuous-Time Models For Production Scheduling In Constrained Subcontracting Conditions

2000· article· en· W2401877381 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2000
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsScheduling (production processes)Production (economics)Computer scienceOperations managementEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

AbstractThis paper focuses on an optimal control approach to continuous-time multi-item scheduling of parallel flexible machines in typical subcontracting conditions. The conditions comprise subcontracting of: constant in-time amount of items along the planning horizon; arbitrary changing in-time amounts; and limited-change in-time amounts of items. Mathematical formulations are presented to model the three typical subcontracting conditions and are studied with the aid of the maximum principle. Based on the properties of the optimal solutions derived, efficient time-decomposition methods are suggested for solving the corresponding problems.RésuméCet article se concentre à la portée de controle optimale pour la prévision de plusieurs articles à temps continuel de quelques machines flexibles parallelement à des conditions caractérisées de sous-traitance. Ces conditions de sous-traitance comprenent: une quantité d’articles constante à temps pendant la durée du plan; des quantités arbitraires changeantes à temps, et des quantités d’articles dont le changement de temps est limité. Les formulations mathematiques sont proposées pour modeler les trois conditions de sous-traitance étudiées à l’aide de principe maximum. En se basant sur les qualités des solutions optimales qui ont été trouvées, des méthodes effectives de la décomposition à temps sont suggerées à la résolution des problèmes correspondants. Additional informationNotes on contributors’ Konstantin KoganKonstantin Kogan obtained his Ph.D. from the Moscow Institute of Mechanization and Power Engineering, Russia. He is currently a Senior Lecturer in the Department of Computer Systems at Holon Center for Technological Education and a visiting Senior Lecturer at the Department of Industrial Engineering at Tel-Aviv University. His research interests are in production control and scheduling of flexible manufacturing systems.

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 score0.117
Threshold uncertainty score0.435

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 communication0.0000.002
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 head0.035
GPT teacher head0.306
Teacher spread0.271 · 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