Energy Efficient Order Picking Routing for a Pick Support Automated Guided Vehicle (Ps-AGV)
Bibliographic record
Abstract
Order picker routing refers to the process of collecting a set of products with the minimum travel time. Recently, a new generation of Automated Guided Vehicles (AGVs) has been developed to assist human order pickers in order to minimize their travel time. These vehicles are using battery as energy source. However, the routing energy efficiency aspect of these systems remains unexplored. Yet any improvement in power consumption will ultimately reduce the DOD (depth of discharge) of the battery and increase its lifespan. For example, in many real AGV applications incorporating the effect of load mass has been neglected, although its importance. In most studies, the methodology proposed for the order picking routing problem does not allow neither the integration of the mass of each Stock Keeping Unit (SKU) nor the calculation of associated energy costs. Those studies are generally limited to ensure that all the items requested by an order are picked up with minimum travel time/distance. In this paper, an Energy Efficient Order Picking Routing algorithm namedEE-OPRis proposed to realize an efficient AGV tour with an acceptable trade-off between energy preservation and travel time minimization. The proposed approach takes into account the mass of loads and its accumulation throughout the pick tour since it intensifies the rolling resistance losses on flat ground, especially at lower speeds. In this regard, an optimization method by means of dynamic states graph is developed. This method is applied to different warehouse layouts. The performance of the suggested algorithm is evaluated by comparing it with an approach minimizing only travel time consumption. Results show that the optimized tours, offered byEE-OPRare effective and robust, with an 18% average saving on the total cost of picking tour.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".