Compression of Project Schedules using the Analytical Hierarchy Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it