Waiting Time Prediction with Invisible Customers
Bibliographic record
Abstract
Problem definition: Motivated by technological advances in real-time data collection about customers location in service systems, we study the effect of partial visibility of customers on waiting time prediction. We consider systems where the predictor observes only a subset of the customers interacting with the system while serving all customers indiscriminately. Methodology/results: We formulate a novel model of a partially visible queue and analyze the waiting time prediction problem, deriving a closed-form expression for the optimal prediction. This facilitates quantifying the performance loss of arbitrary prediction methods because of partial visibility and their inherent limitations (i.e., bias and variance). We compare the performance of a wide range of commonly used predictive methods and examine how partial visibility along with other system parameters affects their performance. We further extend these numerical analyses to queueing systems that exhibit characteristics that are common in practice and that were studied in the service operations literature. Managerial implications: Our analysis shows that the phenomenon of invisible customers profoundly impacts the ability to accurately predict waiting times and should, therefore, be considered an important factor in the development of prediction tools. Such tools cannot be effectively deployed if technological barriers or operational limitations prevent a sufficiently high level of data integrity. This work provides specific insights into the effectiveness of various commonly used prediction methods, some of which are shown to be highly sensitive to partial visibility and other queueing systems characteristics. Our findings suggest that machine learning methods that use carefully chosen features offer the most effective generic solution for waiting time prediction in the presence of invisible customers and explain the mechanisms through which partial visibility deteriorates the performance of prediction methods. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0098 .
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".