Optimal selling price, replenishment lot size and number of shipments for two-echelon supply chain model with deteriorating items
Bibliographic record
Abstract
This paper deals with a pricing and production-distribution model for a deteriorating item in a two-echelon supply chain.The profit function for the manufacturer and retailer in the integrated supply chain is derived.The manufacturer's production batch size is regulated to an integer multiple of the discrete delivery lot quantity to the retailer.The objective is to maximize the total profit per unit time by finding the optimal selling price, production lot size, total cycle time, number of deliveries, and delivery lot size, simultaneously.Based on the notion of optimal interval, we outline an effective algorithm for finding the optimal solution.Finally, the authors present a numerical example to illustrate the theoretical results of the model.Sensitivity analysis for the optimal solution with respect to major parameters is also carried out.The results show that, when the deterioration rate increases, both the optimal production lot size and cycle time decrease.It is interesting to note that an increase in the deterioration rate also tends to reduce the delivery lot size without affecting the number of deliveries per production batch.Also, the optimal interval for N does not change when deterioration rate changes.Reductions in the inventory cycle times for both parties demonstrate the negative effects of deterioration on the supply chain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".