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Record W2954595837 · doi:10.1016/j.procir.2019.04.132

Remanufacturing Network Design for Dual-Channel Closed-Loop Supply Chain

2019· article· en· W2954595837 on OpenAlexaff
Chao Chen, Guoqing Zhang, Jianmai Shi, Yangsheng Xia

Bibliographic record

VenueProcedia CIRP · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRemanufacturingSupply chainClosed loopProfit (economics)Reverse logisticsSupply chain networkChannel (broadcasting)BusinessDual (grammatical number)Industrial organizationComputer scienceSupply chain managementManufacturing engineeringOperations researchEngineeringMicroeconomicsMarketingEconomicsComputer networkControl engineering

Abstract

fetched live from OpenAlex

Closed-loop supply chain (CLSC) is a prominent concept emphasizing on both economic and environmental aspects. A traditional view on these CLSCs for remanufacturing consists of two stages: forward logistics and reverse logistics. There is very little literature considering the impact of dual-channel business on closed loop supply chain network design for remanufacturing. In this research, we consider a CLSC where the remanufacturer needs to satisfy the demands from both online and office channels, A mixed integer nonlinear program is developed to determine the optimal network, and prices of both online and office return, with the objective of maximizing the manufacturer’s profit.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2019
Admission routes1
Has abstractyes

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