The time-cost trade-off problem and its extensions: A state-of-the-art survey and outlook
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
The time-cost optimization is amongst the most critical fields, which has an extensive range of implementation in project scheduling. Achieving a satisfactory balance between these factors can lead to an efficient construction project by reducing both the length of the project and costs at the same time. An effective balance can be achieved using various methods, depending on the situation. This study aims to incorporate the various algorithms used in the last 15 years to reach a satisfying balance between time and cost, including meta-heuristics, heuristics, and exact algorithms. A comprehensive view of the problems associated with time-cost optimization will be provided throughout this review to assist new and challenging researchers who are interested in this type of research. For this purpose, we have reviewed some objective functions and uncertainty techniques that could be employed in time-cost balancing problems. The literature review tables contain a variety of columns, including uncertainties such as fuzzy, probabilistic, interval, robust, and objective functions, along with cost and time, for the investigation of various types of balance issues. In the conclusion of this article, we will show the results of our literature review table using different types of graphic diagrams. For each main column of the table, we will show various types of diagrams to make the results easier to understand.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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