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Record W4404384074 · doi:10.21275/sr22425235646

A Case Study on Planning and Scheduling of a Project PIPLMC of a Package - 6A using Microsoft Project 2016

2022· article· en· W4404384074 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsProject planningProject managementComputer scienceScheduling (production processes)Software engineeringEngineering managementEngineeringSystems engineeringOperations management

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.115
GPT teacher head0.418
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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