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Record W2906625922

Coordination of a Bi-Level Closed-Loop Supply Chain considering Economic and Green Transportation Modes

2018· article· en· W2906625922 on OpenAlexaff
Javad Asl‐Najafi, Saeed Yaghoubi, Amir Azaron

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsSupply chainClosed loopLoop (graph theory)BusinessIndustrial organizationEngineeringControl engineeringMathematicsMarketing
DOInot available

Abstract

fetched live from OpenAlex

In recent years, comprehensive researches have provided ample support for the supply chains in the coordinated decision-making framework. However, the issue of closed-loop supply chain coordination considering various transportation modes has not yet been addressed in the literature. In this paper, a two-echelon closed-loop supply chain consisting of a manufacturer and a retailer is investigated in which the manufacturer acts as a Stackelberg leader and the retailer plays follower role. All transportation activities between the channel members are carried out via two transportation types including the economic and green modes. First, the proposed problem is examined under the decentralized and centralized settings. Then, a mathematical modeling is developed to coordinate the decisions related to retail price, collection effort, and ratio of transportation mode selection. Finally, some numerical examples are applied with the aim of analyzing the performance of decentralized, centralized, and coordinated decision-making structures. The results reveal that not only the Pareto optimal solution is achievable for both channel members but also the coordination scheme has sufficient efficiency to reach the best solution up to the centralized setting.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.157
GPT teacher head0.441
Teacher spread0.284 · 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.

Study designObservational
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

Citations9
Published2018
Admission routes1
Has abstractyes

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