Sensitivity Analysis of Capacitated Facility Location Under Cost Constraints: A Big Data Approach
Bibliographic record
Abstract
The strategic design of supply chain networks and logistics heavily relies on the facility location problem (FLP). To improve the precision and resilience of facility placement decisions in today's data-rich environment, decision-makers are increasingly turning to big data analytics. This research presents a data-driven strategy that addresses a capacitated facility location problem while considering various cost factors and practical constraints. Specifically, the model accounts for fixed costs related to construction and site acquisition, variable operating costs such as wages and electricity, and transportation costs influenced by demand and distance. A key feature of the model is the limitation on the maximum number of warehouses that can be established, reflecting real-world constraints such as strategic policies, land availability, and investment budgets. The model was tested on a real Canadian case study. A comprehensive sensitivity analysis was conducted to evaluate the impact of varying the cap on the number of warehouses on the system's total cost and the configuration of warehouse locations. This analysis used a large-scale dataset representing regional demand, cost parameters, and warehouse characteristics. The results indicate a strong correlation between cost components and facility deployment, with even minor changes in the cost structure or policy leading to significant shifts in optimal outcomes. This study provides valuable insights for supply chain managers and policymakers aiming to optimize logistics operations under uncertainty and practical constraints. It demonstrates how data-driven models, by leveraging big data, can support more informed, robust, and cost-effective facility location decisions. The LINGO software package was used throughout this work.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".