Tactical planning in cooperative two-tier city logistics systems with fairness constraints
Bibliographic record
Abstract
Two-tier city logistics systems (2T-CLSs) offer the potential for more efficient and environmentally friendly freight management. Since such systems require substantial infrastructure and multiple Logistics Service Providers (LSPs) operate within a city, cooperation among LSPs presents opportunities for cost and emissions reductions. However, cooperation requires ensuring every LSP has an incentive to participate and feels fairly treated. To address this, we present a service network design formulation for multi-day tactical planning in a 2T-CLS with cooperating LSPs, incorporating fairness constraints on workload, costs, and service regularity. Through a numerical study, we quantify the impact of fairness constraints, showing that overly strict constraints harm the coalition. Lower cost increases and greater environmental benefits occur when fairness is enforced over multiple days rather than daily. Further, we observe a 47% reduction in CO emissions through cooperation, providing valuable policy implications for LSPs and municipal authorities pursuing sustainable city logistics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".