Bibliographic record
Abstract
Using the net present value is the standard methodology in theoretical analysis, and the most frequently used method for making financial decisions. However, net present value is rarely used in production and inventory decisions. The main reasons appear to be the complexity of the formulae and the robustness of the EOQ model. We investigate the general multiproduct, multistage production and inventory model using the net present value of its total cost as the objective function. A power-of-two heuristic gives us a near optimal solution to this problem. If the base period is fixed (or varied), the solution based on the best power-of-two heuristic will be within 6.2% (or 2.1% ) of the optimal. This result is surprisingly similar to models using the long-term average cost. The average cost does not reflect the time value of money. Does this mean that decisions based on average cost are significantly inferior to those based on net present value? The answer is quite surprising. If we include discounted production cost in the holding cost, it turns out that the decision based on average cost is only 9.6% (in terms of the net present value of the total cost) worse than the decision based on the net present value. However, the reorder interval based on the average cost could be much longer than that derived using net present value. This result shows that average cost is a good approximation to the net present value when the demands are deterministic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".