Project Schedule Compression Considering Multi-objective Decision Environment
Bibliographic record
Abstract
This research aims to present a new method to circumvent the limitations of current schedule compression methods, which reduce schedule crashing to the traditional time-cost trade-off analysis, where only cost is considered. In this research the schedule compression process is modeled as a multi-attributed decision making problem in which different factors contribute to priority setting for activity crashing. For this purpose, a modified format of the Multiple Binary Decision Method (MBDM) and an iterative crashing process are utilized. The developed method is implemented in Visual Basic 2010 environment, with a dynamic link to MS-Project to facilitate the needed iterative rescheduling of project activities. To demonstrate the use of the developed method and to highlight its capabilities, 3 case examples drawn from literature were analyzed. When considering cost only, the generated results were in good agreement with those generated using the Harmony Search method, Genetic Algorithms and iterative crashing process used in original examples, particularly in capturing the project least-cost duration. However, when other factors in addition to cost were considered, as expected, different project least-cost and associated durations were obtained. \nThe novelty of the developed method lies in its capacity to allow for the consideration of a number of factors in addition to cost. Also through its allowance for possible variations in the relative importance of these factors at the individual activity level, it provides contractors with a number of compression execution plans and assists them in identifying the most suitable plan. Accordingly, it enables the integration of contractors’ judgment and experience in the crashing process and permits consideration of different project environments and constraints.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".