A multi-dimensional joint confidence limit approach to mixed mode planning for round-the-clock projects
Bibliographic record
Abstract
Purpose Good planning is key to good project performance. However, for the sub-class of round-the-clock projects requiring mixed mode planning a suitable planning approach does not exist. The purpose of this paper is to develop and validate such an approach. Design/methodology/approach Development of the approach builds on a synthesis and extensions of previous work related to projects with round-the-clock schedules, containing multiple workflows (sequential/cyclical). This approach considers the interdependence among shift-schedule, productivity, calendar duration, and risk registers. It quantifies the confidence in those strategies using a Monte Carlo and a multi-dimensional joint confidence limit (JCL) simulation platform. Findings n of workflows and their interdependencies. Also, the platform results show that the deviation between the deterministic outcomes and the simulated ones are a good indicator when dealing with projects with minimal tolerance for possible imposed mitigation strategies (e.g. round-the-clock projects). Research limitations/implications The validation of the approach is limited to a multi-billion dollar nuclear refurbishment case study and functional demonstration. The applicable class of projects is limited, and includes those for which failure of cost, schedule, or quality implies project failure. Originality/value It is anticipated that the proposed approach will assist with developing a realistic planning strategy by incorporating various factors and constraints under the impact of risks and uncertainty. This may lead to a more reliable determination of outcomes for round-the-clock projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".