Bibliographic record
Abstract
We study three problems in service operations where either the supply or demand is perishable. In the first chapter, we study a perishable inventory system for items whose quality deteriorates in time, e.g., blood products. Specifically, we assume that supply and demand are driven by independent Poisson processes, units have a constant shelf-life, and unsatisfied demand is lost. We consider a threshold-based allocation policy that trades off the age and availability of allocated units. We characterize the sojourn time distribution of units in inventory and evaluate the performance of the threshold policy in terms of the distribution of the age of allocated units and the proportion of outdates and lost demand. Our numerical results demonstrate the importance of system parameters on the performance of the policy and identify important properties of the distribution of the age of allocated units. In the second chapter, we study the performance of certain practical ordering and allocation policies in reducing the age of transfused blood in hospitals while keeping the outdate and shortage rates low. We develop a data-driven (evidence-based) simulation model based on the operations of the blood bank of an acute care hospital in Hamilton, Ontario. We use empirical data to validate our model and estimate its inputs. The results suggest that by properly adjusting the ordering and allocation policies at the hospital level, a significant reduction of issue age could be achieved, without compromising availability or resulting in excessive outdates. In the third chapter, we study the rational abandonment behavior of utility-maximizing customers in the context of an observable priority queue, and identify novel pricing implications. We first characterize the equilibrium abandonment strategy of low-priority customers. We then consider pricing as a means to control the abandonment behavior and investigate its implications on system welfare and firm revenue. A distinguishing feature of our model is that in the presence of abandonment the timing of payment matters. We show that the welfare can be maximized using only a single fee charged upon service completion. In contrast, revenue maximization generally requires a combination of both an entrance and a service fee.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".