Finance-Based Scheduling for Multiple Projects with Multimode Activities
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
Lack of financing and cash deficit is considered as a primary threat to contractor's financial management. Therefore, in case of insufficient cash, many contractors find it difficult to stick with the project schedule leading to extra overhead costs and liquidated damages. Mainly contractors deal with the project scheduling and financing as two independent functions of construction project management. Thus, the main objective of this research is to develop a multi-objective elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for solving finance-based scheduling problem of multi-projects with multi-mode activities. A CPM scheduling model is constructed with its associated cash flow to calculate the values of the multiple objectives. The problem involves the minimization of conflicting objectives: duration of multiple projects, financing costs, and maximum negative cumulative balance. The designed optimization model performs operations of NSGA-II in three main phases: (1) Population initialization; (2) Fitness evaluation; and (3) Generation evolution. An application example is analyzed to illustrate the use of the model and to demonstrate its capabilities in generating optimum solutions. The model can be considered relevant for practitioners to use in large construction projects to make decisions regarding financing.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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