Collaborative supply chain planning and scheduling of construction projects
Bibliographic record
Abstract
In this study, we propose an integrated model for collaborative Construction Supply Chain (CSC) planning that deals with the joint project scheduling and material ordering decisions. The main objective is to achieve more coordination and, therefore, to reduce the total CSC cost. More specifically, we consider a two-echelon Supply Chain (SC) composed of a manufacturer, a warehouse, and multiple construction sites where multiple independent construction projects are planned. The projects require different materials that are provided by the same manufacturer with a limited production capacity. The starting time of each activity is subject to materials availability in construction sites. A mixed-integer linear programming (MILP) model is developed to reduce the total costs while collaboration between contractors is possible. The model is implemented using the IBM ILOG® CPLEX® Optimization Studio and used to analyze the collaboration process through a numerical study to demonstrate the benefits of collaborative planning in construction project management. The decision model help also in finding practical construction projects’ sequences as well as suitable materials ordering, manufacturing, and inventories plans for SC participants.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".