Contracts design for serial delivery with connecting time spot: From a perspective of fourth party logistics
Bibliographic record
Abstract
For a serial delivery system, the latter 3PL needs to be prepared at the transshipment node in advance to reduce the total delivery time. In this paper, we propose the concept of Connecting Time Spot (CTS) to help 4PL schedule the latter 3PL when to wait at the transshipment node. We study a serial delivery system with a 4PL and two 3PLs, where 4PL designs optimal contracts with two types of CTS (GCTS is derived by system parameter and DCTS is determined by 4PL’s optimization) to induce 3PLs to exert the optimal effort levels. We analyze the effects of CTS on the system profit in the centralized system. For the decentralized system, we particularly investigate the optimal contracts in three penalty modes which are according to the occupancy of the warehouses. The results show that CTS can avoid 3PLs’ idle resources and enhance the system profit for serial delivery both in the centralized system and the decentralized system. Compared with GCTS, DCTS has a better performance in enhancing the system profits. Also, the optimal incentive contracts achieve Pareto improvement for system profits. Interestingly, one 3PL’s delivery penalty mode will not affect the other 3PL’s optimal contracts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".