The Inventory Routing Problem with Demand Moves
Bibliographic record
Abstract
Abstract In the Inventory Routing Problem, customer demand is satisfied from inventory which is replenished with capacitated vehicles. The objective is to minimize total routing and inventory holding cost over a time horizon. If the customers are located relatively close to each other, one has the opportunity to satisfy the demand of a customer by inventory stored at another nearby customer. In the optimization of the customer replenishments, this option can be included to lower total costs. This is for example the case for ATMs in urban areas where an ATM-user that wants to withdraw money could be redirected to another ATM. To the best of our knowledge, the possibility of redirecting end-users is new to the operations research literature and has not been implemented, but is being considered, in the industry. We formulate the Inventory Routing Problem with Demand Moves in which demand of a customer can (partially) be satisfied by the inventory of a nearby customer at a service cost depending on the quantity and the distance. We propose a branch-price-and-cut solution approach which is evaluated on problem instances from the literature. Cost improvements over the classical IRP of up to 10% are observed with average savings around 3%.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| 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".