Sustainable inventory models with reduction on environmental emission and ordering costs under the discount policy of prepayment
Bibliographic record
Abstract
This article integrates sustainability principles into a two-echelon supply chain comprising a single-vendor and single-buyer (SV-SB), balancing economic performance and environmental responsibility, as supply chain activities are major sources of environmental emissions. Following that, businesses are compelled to implement additional strategies that simultaneously lower operational costs and minimize environmental emissions. In line with this, the article considers a sustainable economic order quantity (SEOQ) inventory model within a carbon allowance policy. It will investigate the impact of strategic investments designed to reduce ordering and environmental emissions costs. On the other hand, this article considers a vendor who offers price discounts based on the number of advance payment installments. The highest discount is granted with full prepayment in a single installment, whereas partial prepayment yields a lower discount rate, which depends on the number of advance installments. The aim is to determine the optimal replenishment cycle, ordering cost, and environmental emission cost with closed-form solutions under various prepayment discount policies. A solution algorithm is developed to offer guidance for logistics managers on making informed decisions within a sustainable inventory framework. Finally, to demonstrate the model's effectiveness and managerial insights, numerical examples and sensitivity analyses are conducted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".