Integrated location–inventory planning for slow‐moving demands with waiting time limitations
Bibliographic record
Abstract
Abstract This paper studies a stochastic facility location problem integrated with inventory and transportation decisions (SFLPIT) for supply chains with slow‐moving stock‐keeping units (SKUs). Local stores often avoid maintaining such SKUs, resulting in customer demand being fulfilled directly by a central distribution center (DC), where the replenishment lead time may exceed the customer's waiting time limitation (WTL). To reduce waiting times, some local stores can operate as retailer‐based DCs to supply neighbor stores. Demand can be satisfied by the DC, retailer‐based DC, or third‐party logistics company, each with different lead times and procurement costs. We formulate the SFLPIT facing uncertain demand and WTL as a scenario‐based two‐stage stochastic programming model. To solve the proposed model, we improve the sample average approximation (SAA) by integrating a dual heuristic procedure. The experiment results show that the improved SAA achieves significant computation time reduction while maintaining solution quality comparable to the standard SAA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".