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Record W1993209105 · doi:10.1080/19397038.2012.762433

Closed-loop supply chain system with energy, transportation and waste disposal costs

2013· article· en· W1993209105 on OpenAlexaff
Parviz Hasanov, Mohamad Y. Jaber, Simone Zanoni, Lucio Zavanella

Bibliographic record

VenueInternational Journal of Sustainable Engineering · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsToronto Metropolitan University
FundersUniversità degli Studi di Brescia
KeywordsSupply chainContext (archaeology)Environmental economicsSustainabilityProduction (economics)Energy consumptionClosed loopEnergy supplyHolding costBusinessOperations managementEnergy (signal processing)EconomicsMicroeconomicsEngineeringMarketing

Abstract

fetched live from OpenAlex

Energy usage and consumption play important and strategic roles in modern manufacturing, inventory and logistics systems. The literature on inventory models for closed-loop supply chains reveals that, for no clear reasons, energy costs were ignored along with transportation and disposal costs. This paper introduces a closed-loop supply chain model that considers the economic value and energy content of products. It also offers a novel framework for studying lot-sizing policies of production processes in that context. Thus, a mathematical model for a closed-loop supply chain system with energy, transportation and disposal costs is developed. Numerical examples are provided with their results discussed. The developed model was also compared to that of Richter (1996) to stress the importance of accounting for the three noted costs. The numerical results emphasise that accounting for energy, transportation and disposal costs in supply chain modelling increases the sustainability of a production-inventory system due to the strong interdependence of the three costs on one hand, and their relationship to the environment on the other hand.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.002
GPT teacher head0.161
Teacher spread0.159 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2013
Admission routes1
Has abstractyes

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