Integration of DEMATEL, ANP and DEA methods for third party logistics providers’ selection
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.
Bibliographic record
Abstract
In a competitive environment many companies usually outsource their logistics functions to the Third Party Logistics (TPL) providers to focus on their core businesses. However, the selection of proper TPL provider is not an easy task because of conflicting quantitative and qualitative criteria. This study presents an integrated model based on three well-known methods Decision Making Trial and Evaluation Laboratory (DEMATEL), Analytical Network Process (ANP) and Data Envelopment Analysis (DEA) for the evaluation and selection of TPL providers. DEMATEL computes the effects between selection criteria while ANP derives the weights of each criterion related with TPL providers' selection problem. Finally DEA presents a mathematical model for ranking TPL providers alternatives with respect to various criteria. The application of integrated model is demonstrated with a case study. The novelty of this study comes from the fact that there is no research in the literature integrating DEMATEL, ANP and DEA for the TPL selection problems.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it