Automation of Project Planning and Resource Scheduling on a Rough Grading Project
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
Traditional earthwork projects rely heavily on project managers and superintendents to generate the truck hauling plan based on experiences. Though various linear programming methods have been proposed to produce cost effective plans specifying each job with source, destination, and volume of material to be transported, how to sequence these haul jobs tends to be neglected. As such, temporal and spatial conflicts between jobs are commonplace in resulting construction plans. On the other hand, establishing linear equations requires considerable expertise and time. This could hamper the updating of the project plan due to the rapidly changing situation on the earthmoving site. In this paper, a fully automated flow network based project planning method is applied to: 1) optimize the earthwork operation; 2) define conflict-free haul jobs; and 3) produce an activity-on-node (AON) network model. Further automation can be achieved through integration with resource constrained scheduling. The clearly defined haul jobs and the AON network will serve as input to the computer platform of simplified discrete event simulation approach (SDESA) for schedule optimization subject to resource availability limits. The complete automation approach is applied to a rough grading case based on a real-world project in northern Alberta, Canada. The case study shows the proposed approach is intuitive to communicate and implement in the field. A comparison is also done between the plan generated by automation and the plan proposed by the superintendent. The result shows considerable savings on both haul effort and total cost.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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