Dynamic Task Allocation in Intelligent Warehouses with Hybrid Workforce of Automated Guided Vehicles and Human Pickers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it