Dynamic Task Allocation in Intelligent Warehouses with Hybrid Workforce of Automated Guided Vehicles and Human Pickers
Bibliographic record
Abstract
This article explores the integration of Automated Guided Vehicles (AGVs) in warehouse order picking, a crucial and cost-intensive aspect of warehouse operations. The booming AGV industry, accelerated by the COVID-19 pandemic, is witnessing widespread adoption due to its efficiency, reliability, and cost-effectiveness in automating warehouse tasks. Through the strategic use of AGVs, this article focuses on enhancing the picker-to-parts system, which involves workers travelling to item locations, collecting them, and moving to the next location. We propose a novel MDP model for coordinating a hybrid team of human and AGV workers, aiming to maximize order throughput and operational efficiency, and employ a Neural Approximate Dynamic Programming (NeurADP) approach as the solution method. Specifically, our solution framework involves innovative solutions for non-myopic decision making, order batching, and battery management. The numerical results demonstrate that the NeurADP policy outperforms all benchmark policies, including both myopic and non-myopic ones, with a 3.32% and 5.44% improvement in order fulfillment over the alternatives. Comprehensive empirical analysis offers valuable insights for managing a heterogeneous workforce in a hybrid warehouse setting, highlighting the contributions of our work to the field of warehouse automation and logistics.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".