Goal Programming and Monte Carlo Simulation for Optimizing Inbound Scheduling in Resource-Constrained Warehouses
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
Resource efficiency lies at the heart of logistics performance, with unloading operations in storage facilities serving as a critical determinant of overall productivity. In less developed regions, the widespread reliance on basic rules-based systems such as FIFO often proves inadequate for handling operational complexities, leading to bottlenecks and inefficiencies. Small and medium-sized enterprises (SMEs), constrained by limited resources, are compelled to optimize existing infrastructure rather than invest in costly upgrades. To address this challenge, the present study introduces a goal-oriented programming model designed to assign trucks to loading docks within specific time slots, thereby enhancing time efficiency. The model evaluates performance across four key metrics: waiting time, loading time, overtime, and equity. By leveraging goal programming, numerical prioritization of these objectives becomes possible, enabling flexible adjustments to meet operational needs. Furthermore, Monte Carlo simulation (MCS) is employed to incorporate variability into the dataset and assess model robustness under real-world uncertainty. Experimental results reveal that the proposed approach consistently outperforms traditional systems, delivering significant improvements in time efficiency. These findings highlight the potential of goal programming as a practical solution for planning in resource-constrained environments. The resulting model offers an adaptive, reliable framework that warehouse managers can implement without incurring substantial infrastructure costs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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