A FRAMEWORK FOR SPATIO-TEMPORAL UNCERTAINTY-AWARE SCHEDULING AND CONTROL OF LINEAR PROJECTS
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
Linear repetitive projects, which are resource-driven in nature, are characterized by a series of repetitive activities in which the resources share the same space either in sequential or parallel manner. The frequent movement of resources over limited shared space needs to be well-planned to avoid potential issues during the execution of linear projects. As such, schedules developed for these projects needs not only to take into account all the logical, project-dependent and precedence constraints of activities but also to incorporate the space and time constraints that co-exist for the movement of thei8r resources. Negligence in incorporating spatial and temporal constraints in developing and improving schedules of linear projects increases the risk of delays and workspace congestions that can substantially hinder the performance of the activity resources. The study presented here proposes and develops an uncertainty-aware scheduling and control framework for linear projects to address the needs mentioned above. For this purpose, first, a new type of float was introduced as the Space-Time Float. The Space-Time Float is an envelope for all possible movement patterns that a linear activity or its associated resources can take considering the time and space constraints of that activity. The next endeavor in the development of the uncertainty-aware linear scheduling and control framework was to augment the current linear scheduling methods by presenting an uncertainty-aware optimization method to optimize the duration of linear projects while minimizing their potential congestions. A constraint satisfaction approach was used for the two-tier optimization of duration and congestion, and a fuzzy inference system was incorporated to assess the inherent uncertainty in linear activities. A new type of buffer, Uncertainty-Aware Productivity Buffer is also introduced to account for the uncertainties inherent in project activities. Spatial progress of activities needs not only to be considered in the planning phase but also to be closely monitored during construction. The framework presented in this study also applies to the monitoring and control of linear projects. While most of the current methods still do not accommodate real-time bi-directional control of linear projects, this framework is based on the Cyber-Physical Systems (CPS) architecture and bi-directional communication of data. To this end, a CPS-based application for Earned Value (EV) monitoring and control of road and highway projects is presented. Different steps of the generated framework are validated through various literature and field-based case studies. The results demonstrate the effectiveness of the presented method in planning and control of unforeseen variations from the planned schedules of linear projects. As such, the present study contributes and adds to the current body of knowledge of linear projects by presenting an efficient scheduling and control framework that takes into account logical, spatio-temporal and project-based constraints of linear activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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