Penalty and Threshold Optimization of the Retailer - Vendor Return Contracts for Contract Re-Negotiation in Retail Reverse Supply Chains
Bibliographic record
Abstract
In this paper, we consider a decentralised reverse supply chain constituting of multiple vendors and an independent retailer. The vendors offer the retailer return contracts with a multi-layered penalty structure deal. We focus on the strategic decision of developing optimal vendor re-negotiation contract parameters for the retailer. We model the problem as a mixed integer nonlinear program (MINLP) where the retailer decides on the vendor penalty fees and return thresholds simultaneously. We propose an efficient solution approach based on decomposing by decoupling the decision on penalty fees and return thresholds. The resulting problems are linear and we used them to provide rules for re-negotiation tactics for the retailer. We find that the retailer can save up to 7% from re-negotiation their contract terms. [Received: 16 June 2022; Accepted: 26 February 2023]
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".