A two-phase model for resilient hub and mobile distribution centers location
Bibliographic record
Abstract
Hub location is crucial for resilient and uninterrupted supply chain operations, particularly during disruptions or unforeseen events. In this paper, we propose a resilience hub location framework for Third Party Logistics (3PL) companies with two key objectives: optimizing demand flows and establishing a resilient network capable of with-standing sudden disruptions. The study aims to identify the key criteria that contribute to the successful implementation of the resilient center. The proposed structure utilizes a two-phase decision-making methodology. The first phase presents a new Multi-Criteria Decision-Making (MCDM) approach called SWARA-EDAS method that evaluates and ranks potential locations based on resiliency criteria. The second phase proposes an optimization model to determine the optimal hub location. To illustrate the approach, a real-world case study of a 3PL company in Tehran is included. Due to the absence of precise demand data in the case study, a novel clustering approach is proposed to estimate the demand flow. Each individual cluster can be considered as a distinct demand point, and a clustering analysis involving 122 regions within Tehran is conducted, taking into account various factors such as population, economic index, accessibility to the Internet, and number of business units. To enhance the resiliency of the network, mobile distribution centers are also deployed. These mobile centers not only provide flexibility but also serve as backup capabilities in the event of a disruption or failure at the fixed hub. The proposed structure offers practical in-sights for 3PL companies seeking to implement a resilient network structure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".