Mathematical model, heuristics and exact method for order picking in narrow aisles
Bibliographic record
Abstract
Order picking is one of the most challenging operations in distribution centre management and one of the most important sources of costs. One way to reduce the lead time and associated costs is to minimise the total amount of work for collecting all orders. This paper is motivated by a collaboration with an industrial partner who delivers furniture and electronic equipment. We have modelled their narrow aisles order picking problem as a vehicle routing problem through a series of distance transformations between all pairs of locations. Security issues arising when working on narrow aisles impose an extra layer of difficulty when determining the routes. We show that these security measures and the operator equipment allow us to decompose the problem per aisle. In other words, if one has to pick orders from three aisles in the warehouse, it is possible to decompose the problem and create three different instances of the picking problem. Our approach yields an exact representation of all possible picking sequences. We also show that neglecting 2D aspects and solving the problem over a 1D warehouse yields significant difference in the solutions, which are then suboptimal for the real 2D case. We have solved a large set of instances reproducing realistic configurations using a combination of heuristics and an exact algorithm, minimising the total distance travelled for picking all items. Through extensive computational experiments, we identify which of our methods are better suited for each aisle configuration. We also compare our solutions with those obtained by the company order picking procedures, showing that improvements can be achieved by using our approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".