Bilevel Programming Approach to Optimizing a Logistic Distribution Network with Balancing Requirements
Bibliographic record
Abstract
Traditional approaches to a location allocation problem have focused on the allocation of customers to a distribution center (DC) according to some arbitrary geographical boundaries (e.g., administrative zones and census districts), which usually incurs underuse or overcrowding of these centers. Location allocation with balancing requirements (e.g., balanced workload of service among DCs) has therefore been addressed. A distribution strategy with balanced-workload allocation aims to be cost-efficient and to improve customer service. A novel bilevel programming model is presented that minimizes the cost of the total distribution network and at the same time balances the workload of each DC for the delivery of products to its customers. A genetic algorithm-based approach was developed to cope with the bilevel model, and it was tested on a best realistic data set. In addition to the most cost-efficient design, the bilevel programming model presents a picture to decision makers that shows the trade-off between the objective of cost minimization and the balancing requirements. It is also shown that the bilevel model offers a flexible framework that allows the incorporation of more requirements and constraints if necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".