Path-Float Based Approach to Optimizing Time-Cost Tradeoff in Project Planning and Scheduling
Bibliographic record
Abstract
In theory, time-cost tradeoff (TCT) analysis is a classic planning problem appealing to construction management; yet, existing analytical methods are found inadequate to make a significant impact in practice. Heuristic methods lack a theoretical basis to ensure arriving at optimum solutions in solving specific problems; on the other hand, mathematical programming requires cumbersome, complicated formulation. This study proposes a new computing framework for TCT optimization that takes advantage of a path-float based technique and integer programming (IP). The project duration can be shortened in each crashing cycle based on path-float analysis; while IP is nested to inform on which activities on the critical path(s) to shorten by how long duration. The new TCT optimization approach streamlines critical path analysis in each cycle and finds global optimums in terms of lowest project cost or shortest project duration. Because only part of the network (critical path) is considered in IP formulation in each intermediate cycle, the complexity of IP formulation plus the search space is substantially reduced. A case study is used to verify the proposed method and demonstrate its application. The proposed method can be automated to tackle large project networks commonly encountered in workface planning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".