Pricing and matching for on-demand platform considering customer queuing and order cancellation
Bibliographic record
Abstract
Queuing on an on-demand platform may make some customers disgust and give up using it, and the customers who have confirmed the orders may also cancel the orders due to some uncertain factors, which causes certain opportunity loss to the platform. This article considers both customer queuing and order cancellation (COC) behaviour, and studies optimal pricing and matching of the profit-maximizing platform. We first construct models without and with COC behaviour (cases N and C), and then propose two strategies of the platform to deal with COC behaviour, including the penalty strategy (case PC) and the penalty-subsidy strategy (case PSC). By solving these models and analysing, we find that although the penalty strategy intuitively discourages some customers from using on-demand services, the platform reduces the service price because of penalty fee, which indirectly encourages more customers who may not cancel orders to request services. We also find that when the COCR is greater than a certain critical point, both the penalty strategy and penalty-subsidy strategy are advantageous, while the penalty strategy is the best. However, when the COCR is less than the critical point, the penalty strategy is unfavourable, while the penalty-subsidy strategy is advantageous.Abbreviations: COC: customer queuing and order cancellation; COCR: customer order cancellation rate.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".