Determination of Logistics Distribution Centers: A Combination of Spatial Analysis and Analytical Hierarchy Process
Bibliographic record
Abstract
The strategic siting of logistics distribution centers, particularly within the medical sector, has increasingly emerged as a crucial consideration in optimizing the supply chain.This study focuses on identifying the most advantageous location for a new logistics distribution center in Aceh Province, leveraging the integration of the analytical hierarchy process (AHP) and geographic information systems (GIS) overlay techniques.Parameters were weighted using AHP, and spatial analysis facilitated the classification of zones into three suitability categories: low, moderate, and high.It was determined that the optimal location for the establishment of a new center would be within a high suitability zone.Six potential sites, designated as Locations A through F, were initially identified.Subsequent evaluation, which included considerations of access road availability and the capacity to uniformly service all health-related warehouses, led to the selection of Location C as the most ideal.This selection underscores the importance of comprehensive spatial and hierarchical analysis in the decision-making process for logistics operations, ensuring effective distribution networks within critical sectors such as healthcare.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".