Sustainable robotic mobile fulfillment system with pod repositioning in warehousing 5.0
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 paper explores energy-efficient operations in Robotic Mobile Fulfillment Systems (RMFS) by jointly optimising order assignment, pod selection, and pod repositioning under a wave picking strategy. In line with Warehousing 5.0 objectives, the aim is to reduce energy consumption through the intelligent coordination of robotic movements while ensuring workload balance and operational feasibility. We first propose a multi-period, integrated optimisation model with perfect foresight of future demand, serving as a theoretical benchmark. Recognizing the limitations of this assumption in practice, we develop three alternative methods: (i) a two-phase myopic approach that decouples assignment and repositioning; (ii) an integrated myopic model that solves them jointly; and (iii) a two-stage stochastic programming model that captures demand uncertainty through scenario sampling. To enhance scalability, we introduce a local search matheuristic that improves myopic solutions by exploring repositioning options under expected demand. Computational experiments based on realistic RMFS configurations demonstrate the value of incorporating pod repositioning into the decision process. Results show that the integrated and stochastic models yield notable energy savings compared to sequential approaches, offering actionable insights for sustainable automation in warehouse operations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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