Compression of Project Schedules using the Analytical Hierarchy Process
Bibliographic record
Abstract
ABSTRACT This paper presents a new method for schedule compression of construction projects using the analytical hierarchy process (AHP). The method utilizes a multi‐objective decision environment in which activities are queued for crashing based on the priorities established in that environment. Schedule compression is commonly needed in management of engineering, procurement and construction projects. A wide range of methods are introduced in the literature to perform schedule compression utilizing genetic algorithms, heuristic rules, near‐optimum solutions using harmony search and analogy with the direct stiffness method for structural analysis. Although all these methods consider only cost in the process of schedule compression, a recently conducted survey, by the authors, indicates that project managers consider more than one factor in this process. In fact, the lack of consideration of factors beyond cost has been attributed to the limited use of existing methods. The method presented in this paper aims to circumvent the limitation of the existing methods. It utilizes the findings of a recently conducted survey questionnaire as well as the AHP to develop a multi‐objective decision environment to perform schedule compression in a practical and flexible manner. It further allows for consideration of risk associated with the individual attributes considered in setting priorities for activity crashing. A numerical example is analysed to demonstrate the use of the developed method and to illustrate its practical features. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".