Joint capacity, inventory, and demand allocation decisions in manufacturing systems
Bibliographic record
Abstract
We study the demand, inventory, and capacity allocation problem in production systems with multiple inventory locations and a production facility operating under linear and concave costs. Independent stochastic demand from multiple sources is fulfilled from multiple warehouses that are in turn replenished from a shared production facility with stochastic production lead times. We propose a novel formulation of the demand allocation problem, and show that the optimal customer allocations are not necessarily single-sourced. The new formulation allows the inclusion of additional decisions alongside demand and inventory allocation. Capacity decisions are incorporated under two cost structures: linear and concave. For the concave case, we show that for a given demand and inventory allocation, the optimal capacity of the production facility takes on discrete values within a finite set, which allows the objective to be linearized. We demonstrate numerically that the joint optimization of capacity, inventory, and demand allocation decisions has significant cost savings over a sequential decision and leads to a high utilization of the production facility under linear capacity costs, but relatively low utilization under concave costs. Safety stock, on the other hand, at the distribution centers is relatively low under linear and concave cost.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".