A stochastic interval algebra for smart factory processes
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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 presents a stochastic interval algebra specifically developed to evaluate the time and cost properties of smart factories. This algebra models production tasks as intervals and treats allocation and scheduling as algebraic operations on these intervals, with the goal of analysing the impact of resource allocation decisions on total production time or economic cost. The theoretical foundations of this notation are introduced, and then several simple examples of their use are presented. The proposed algebra can be also applied to describe multi-stage production and service processes, recorded with an activity-on-arrow type of graphs, In addition, it was analysed a real-life application of the described technique to planning and scheduling the activities in restaurants preparing takeaway meals. The data was collected in 30 restaurants throughout Poland, using a bespoken software/hardware Kitchen Delivery System, in which over 65,000 orders were registered. Time criteria for the correctness of individual stages of meal preparation were proposed and, after filtering out incorrect orders, the appropriate probability distributions were fitted to the remaining measured activity durations. The resulting probabilities can then be used in practice to improve the accuracy of predicting the completeness of food preparation, which in turn should improve food delivery planning with greater accuracy and enable more accurate order delivery times to be provided to end customers
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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