Warehouse Network Design With Fortification Considerations: A Mixed-Integer Nonlinear Modeling Approach
Bibliographic record
Abstract
This study presents a mathematical model for optimizing warehouse-location decisions in supply-chain network design. Previous studies have focused on the facility-location problem and provided fortification plans and solutions. However, these studies did not consider multi-capacity warehouses. The proposed model identifies optimal warehouse locations, sizes, and branch-warehouse assignments. Additionally, it determines fortification measures to mitigate the risk of warehouse failure. The model is formulated as a mixed-integer nonlinear problem and subsequently linearized using standard techniques combined with a proposed method based on average-probability approximation. A case study of a Canadian company illustrates the model’s efficacy. Sensitivity analysis results demonstrate that when the numbers of fortified and built warehouses are equal, failure probability has no effect on the objective function of the model. Thus, this study presents a comprehensive model that assists businesses in decision-making regarding their warehouse operations, aiming to lower the risk of failure and optimize warehouse infrastructure utilization.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".