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Record W4312897731 · doi:10.1016/j.ifacol.2022.09.574

A flexible closed loop supply chain design considering multi-stage manufacturing and queuing based inventory optimization

2022· article· en· W4312897731 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

VenueIFAC-PapersOnLine · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFlexibility (engineering)Supply chainQueueing theoryComputer scienceSupply chain networkGenetic algorithmProduct (mathematics)Holding costClosed loopQueueSensitivity (control systems)Supply chain managementMathematical optimizationOperations researchEngineeringControl engineeringBusinessEconomicsMathematics

Abstract

fetched live from OpenAlex

In this study, a multi-objective nonlinear model is used to design a closed-loop supply chain network. We consider a multi-period, multi-product and multi-stage manufacturing model by employing a queuing system for inventory management of finished and return products under demand uncertainty limitations. The purpose of this paper is to simultaneously reduce the waiting times of queues in warehouses and the costs of production, transportation, inventory and queuing systems. The flexibility of the chain is formulated at three concepts: product flexibility, the flexibility of supply against demand fluctuations, and the flexibility of time to satisfy all demand. The proposed non-linear model is solved by a robust genetic algorithm. A sensitivity analysis of the flexibility ratios reveals several insights for practitioners and academics.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.245
Teacher spread0.202 · 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