Integrated construction supply chain: an optimal decision-making model with third-party logistics partnership
Bibliographic record
Abstract
Studies have confirmed the benefits of using Third-party logistics (TPL) for efficient construction management, especially in large projects. Nevertheless, there is a dearth of decision-making models evaluating the exact role of TPL providers as drivers for supply chain (SC) integration and optimisation. This study aims to develop a decision-making model for construction supply chain (CSC) optimisation, with possible TPL integration. The proposed model considers two types of purchased materials (type-1 and type-2) and assists the main contractor in determining construction supply chain management (CSCM) strategies, including supplier selection, order quantity determination, and TPL use evaluation. Using the model, the main contractor can take advantage of the TPL provider’s warehouse and order larger quantities, if necessary, to obtain lower prices offered by suppliers. Through case examples in Canada, we find that the proposed model performs better in optimising total SC cost, as compared to the model without TPL. Model validations also show that TPL can be conditionally used to improve construction logistics performance and to meet practical requirements targeting issues in the construction industry.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".