Resource Supply-Demand Matching Scheduling Approach for Construction Workface Planning
Bibliographic record
Abstract
This paper discusses a novel approach for resource-based scheduling that builds upon existing analytical models to achieve practical allocation of resources that are constrained by supply and demand. The approach facilitates workface planning to allocate work to individual craft persons. The main contribution of the work is advancement of the current scheduling practice of workface planning by (1) generalizing the resource supply-demand matching problem (RSDMP); and (2) formalizing the RSDMP scheduling approach for planning resource workflows which consists of (a) the mathematical model, (b) a two-stage optimization approach, and (c) innovative use of a resource-activity interaction table. The result is an optimum resource-constrained schedule providing the shortest project duration with the leanest resource supply. The optimum resource requirement is identified between the lower and upper bounds of the resource supply limit, thus ensuring the site’s spatial and safety requirements. The optimum resource workflows of individual craft persons are presented to facilitate the project execution at the workface level. To illustrate the approach, an example adapted from a classic textbook is used. To demonstrate the effectiveness of the approach on a practical level, an oil refinery turnaround project including input data and relevant information prepared for workface planning is used. As a result of the RSDMP analysis, the optimum plan obtained is resource-loaded, practically feasible, and workface executable.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".