Credit financing in economic ordering policies for non-instantaneous deteriorating items with price dependent demand under permissible delay in payments: A new approach
Bibliographic record
Abstract
In the existing literature of inventory modeling under the conditions of permissible delay in payments, researchers have assumed that the retailers have to settle their accounts at the end of credit period i.e. supplier accept only full amount at the end of the credit period.However in reality, supplier may either accept the partial amount at the end of the credit period and unpaid balance subsequently or the full amount at a fix point of time after the expiry of the credit period, if the retailer finances the inventory from the supplier itself.Further, in the classical deteriorating inventory models, the common unrealistic assumption is that all the items start to deteriorate as soon as they arrive in the system.However, in realistic environment, it is observed that there are several non-instantaneous deteriorating items that have a shelf life and start to deteriorate after a time lag, like dry fruits, potatoes, yams and even some fruits and vegetables etc. Considering the importance of above mentioned facts, the present study formulates a fuzzy inventory model for non-instantaneous deteriorating items under conditions of permissible delay in payments.The paper discusses all the possible cases which may arise and yet not considered in the previous inventory models under permissible delay in payments.Further, this paper also considers pricedependent demand and the possibility of higher interest earn rate than interest payable rate.The objective of this study is to determine the optimal decision policies for the retailer which maximizes the total profit.Finally, the numerical examples are solved by using the proposed algorithm to show the validity of the model followed by the sensitivity analysis.
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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 | 0.004 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".