Analysis and improvement of delivery operations at the San Francisco Public Library
Bibliographic record
Abstract
Abstract Urban public library systems have always transported and delivered library materials within their branch systems. In recent years, however, the introduction of internet‐based, online library catalog systems has allowed users to search the library's catalog, select and reserve a book or a video and have it delivered to the branch of their choice. Consequently, the demand for delivery services is increasing at rapid rate in large urban public libraries systems. Having experienced a similar growth in the demand for delivered items, the San Francisco Public Library (SFPL) commissioned a study to improve its delivery operations. Using operations management concepts, such as pre‐sorting of material to avoid double handling, cross docking to reduce cycle time of delivery, and workload balancing among delivery routes to effectively increase delivery capacity, the delivery operations were restructured. We developed optimization models for library delivery operations that specifically accounted for pre‐sorting, cross docking and route balancing. We also developed heuristics for solving these models and implemented them to redesign the delivery operations at SFPL. The redesigned delivery operations will reduce the cycle time and the cost of delivery by almost half. Furthermore, through balanced utilization of existing truck capacities, the delivery operations will be able to handle significantly larger delivery volume and thereby accommodate future delivery service growth without additional investments. The operations management concepts and techniques illustrated in this paper through the example of SFPL should prove to be useful to other urban, multi‐branch library systems as they deal with their delivery challenges.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".