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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".