Optimal ordering policy for an integrated inventory model with stock dependent demand and order linked trade credits for twin ware house system
Bibliographic record
Abstract
Many entrepreneurs use different ways to offer credit to enhance long term profit as well as relations with their customers.Order-size dependent credit period is one of them that encourage customers to order large lots to grab more credits in payments.Since display of items play positive role in boosting demand, stock dependent demand is assumed in the proposed model.Ordering large lots can create space issue so the proposed model presents a rented warehouse along with owned one.Rented warehouse is used only when owned warehouse is utilized, completely.Here we propose an integrated inventory model with capacity utilization dependent holding cost to optimize joint profit of supplier and retailer.An algorithm is developed to determine the optimal replenishment policies in order to enhance total profit of supply chain under different ordering schemes.Total joint profit for supplier and retailer is optimized using MATLAB 2015.Numerical examples are presented to illustrate the solution procedure and the results.Sensitivity analysis for some key parameters is carried out to demonstrate the influence of different parameters on over-all profit and cycle time.The proposed model is applicable to fast moving consumer goods (FMCG) and home textile industry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".