Picking Operations in Warehouses With Dynamically Arriving Orders: How Good is Reoptimization?
Bibliographic record
Abstract
ABSTRACT E‐commerce operations are essentially online, with customer orders arriving dynamically. However, very little is known about the performance of online policies for warehousing with respect to optimality, particularly for order picking and batching operations, which constitute a substantial portion of the total operating costs in warehouses. We aim to close this gap for one of the most prominent dynamic algorithms, namely reoptimization (Reopt) , which reoptimizes the current solution each time a new order arrives. We examine Reopt in the Online Order Batching, Sequencing, and Routing Problem (OOBSRP) , in both cases when the picker uses either a manual pushcart or a robotic cart. Moreover, we examine the non‐interventionist Reopt in the case of a manual pushcart, wherein picking instructions are provided exclusively at the depot. We establish analytical performance bounds employing worst‐case and probabilistic analysis . We demonstrate that, under generic stochastic assumptions, Reopt is almost surely asymptotically optimal and, notably, we validate its near‐optimal performance in computational experiments across a broad range of warehouse settings. These results underscore Reopt 's relevance as a method for online warehousing applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".