Incentive-Compatible Revenue Management in Queueing Systems: Optimal Strategic Delay
Bibliographic record
Abstract
How should a firm design a price/lead-time menu and scheduling policy to maximize revenues from heterogeneous time-sensitive customers with private information about their preferences? We consider this question for a queueing system with two customer types and provide the following results. First, we develop a novel problem formulation and solution method that combines the achievable region approach with mechanism design. This approach extends to menu design problems for other systems. Second, the work conserving cμ priority rule, known to be delay cost minimizing, incentive-compatible, and socially optimal, need not be revenue maximizing. A strategic delay policy may be optimal: It prioritizes impatient customers, but artificially inflates the lead times of patient customers. This suggests a broader guideline: Revenue-maximizing firms that lack customer-level demand information should also consider customer incentives, not only operational constraints, in their scheduling policies. Third, we identify general necessary and sufficient conditions for optimal strategic delay: a price, a lead-time, and a segment-size condition. We translate these into demand and capacity parameter conditions for cases with homogeneous and heterogeneous valuations for each type. In some cases strategic delay is optimal if capacity is relatively abundant, in others if it is relatively scarce.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| 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".