Dynamics between warehouse operations and vehicle routing
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Bibliographic record
Abstract
When scheduling the distribution of ordered items from a warehouse to customers, the transportation planning is generally done first and serves as input for planning warehouse operations. Such a sequential approach can lead to substantial inefficiencies when the customer deliveries are restricted by time windows, and the warehouse has limited resources available (both order pickers and space in the staging area). This paper studies the trade‐offs between warehouse operations and transportation planning. The goal is to understand the impact of three specific managerial interventions: adopting an integrated planning approach, expanding the available staging space, and expanding the delivery time windows. To this end, we propose a mathematical model for a general vehicle routing problem that incorporates order batching, order picker scheduling, staging, and vehicle loading. We introduce a novel idea to express the picking time of an order batch as a function of the batch size and develop a metaheuristic to solve this integrated problem. Furthermore, we develop exact algorithms to provide optimal solutions for the individual warehouse and transportation problems in a sequential planning approach. Managerial insights are distilled from case studies in two warehouses, one for ambient products and the other for refrigerated products, of a leading grocery retailer in the Netherlands. Our results show that integrated planning outperforms the other managerial interventions and generates cost savings between 9% and 11%. Savings are generally realized by executing larger order batch sizes to be picked in the warehouses at the expense of additional routing cost (around 2–3%). The second intervention in the form of time window expansions of only 15 minutes for customer deliveries can lead to cost savings between 4% and 6%, which results from a reduction in both transportation and warehousing cost. Expanding the capacity of the staging area is only meaningful when the staging space is highly utilized and only results in cost savings for the 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 | 0.000 | 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