A Case Study on Planning and Scheduling of a Project PIPLMC of a Package - 6A using Microsoft Project 2016
Bibliographic record
Abstract
There have been many case studies/review papers in area of project planning and scheduling from last few years. Various construction activities are managed to achieve the profit within limited funds, resources, and time. In project management there are many techniques are used for scheduling and coordinating the various resources by controlled method. Management techniques such as Critical Path Method (CPM), Program Evaluation and Review Techniques (PERT) have been successfully implemented prior to the 1970?s, in various construction projects in the countries like Canada, USA, Japan, Australia, etc. These techniques are helpful to manage in efficient and economic use of resources for completion of project objectives with limitless availability of resources, though it is observed that resources are limited in real time project scenario. While the focus of this paper will be on the project schedule of Polavaram Irrigation Project of Left Main Canal (PIPLMC) of Package-6A of 25.78KM from KM 111.000 to KM 136.780 (Part Work) by using MS Project 2016. This paper will briefly present an overview of the scheduling the project duration using software MS Project 2016. In this project, preparing an accurate and workable plan is very difficult. In this project by using project management technique like priority rule-based scheduling method used to resolve resource conflicts and useful in minimizing the project duration within limited availability of resources and time to make the project profitable and within project duration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".