Sustainable robotic mobile fulfillment system with pod repositioning in warehousing 5.0
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".