Integrating storage allocation with manual order picking and replenishment operations in a distribution centre
Bibliographic record
Abstract
This paper introduces a mathematical programming formulation and a simulation-based heuristic for the allocation of storage positions to products picked by human operators on man-aboard vehicles traveling through the warehouse of a wholesale company. In this problem, the tactical level of the assignment decisions affects the operational level of the picking process. We propose a simulation-optimisation framework that integrates the two. Our formulation of the storage location assignment problem also handles the constraint according to which a picking position should be paired with a (vertical) replenishment position for a given item. To solve realistic instances, we design an iterated local search (ILS) metaheuristic with an embedded discrete-event simulator (DES) that evaluates the most promising moves at each iteration. The DES allows reproducing the handling operations performed by multiple order pickers under uncertainty, mutual interferences and congestion-related phenomena. Overall, the flexible simulation-optimisation (SO) framework evaluates the operational times and daily productivity of the order picking organisation. Numerical results are presented for real data, under an S-shape picking policy with a skip-and-go rule to deal with lacking items. Under a proper tuning of the ILS parameters, the SO framework allows to achieve a nearly 17% improvement in warehouse productivity.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".