Optimizing Warehouse Location and Fortification: A Mixed-Integer Nonlinear Modeling Approach
Bibliographic record
Abstract
Abstract This study presents a mathematical model for optimizing warehouse-location decisions in supply-chain network design. Previous studies have modeled 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, and it determines fortification measures to mitigate the risk of warehouse failure. The model is formulated as a mixed-integer nonlinear problem and is subsequently linearized using standard linearization techniques combined with a proposed method based on the average-probability approximation. A case study of a Canadian company is used to illustrate 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 can be used to assist businesses in decision-making regarding their warehouse operations to lower their risk of failure and optimize their use of warehouse infrastructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".