Efficient last-mile logistics with service options: A multi-criteria decision-making and optimization methodology
Bibliographic record
Abstract
The rapid growth of online shopping has intensified the need for cost-effective and efficient delivery systems, posing a significant challenge for businesses worldwide. This study proposes an innovative two-phase methodology that uses a hybrid multi-criteria decision-making (MCDM) approach for efficient last-mile logistics with service options (ELMLSO) such as home delivery, self-pickup, and differently-priced services. This approach aims to streamline last-mile logistics by integrating these service options, resulting in a more comprehensive and effective delivery network that enhances customer satisfaction and maintains a competitive edge. The first phase employs the Ordinal Preference Analysis - Evaluation based on Distance from Average Solution (OPA-EDAS) method to select optimal pickup and delivery centers. The second phase identifies the optimal route using a bi-objective mixed-integer mathematical model, striving to balance cost minimization and customer satisfaction maximization. The Normalized Normal Constraint Method (NNCM) is utilized to solve this model. The application of these methods results in considerable cost savings and improved customer satisfaction, offering valuable insights for managers within the last-mile logistics industry.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".