A novel decision support system for integrating supply chain and project management decisions to optimize multiple project performance: a case study in building renovation
Bibliographic record
Abstract
Effective construction supply chain (CSC) management remains a critical challenge, particularly during pre-construction, where coordination across multiple projects is essential. While previous research has explored planning and scheduling in isolation, limited attention has been given to integrating master planning and scheduling with detailed planning and scheduling within a unified framework. This study addresses this gap by developing a novel decision support system (DSS) that combines heuristic methods with a mixed-integer linear programming (MILP) model to optimize integrated CSC planning for multi-project environments. The DSS supports strategic decisions involving resource allocation (renewable and non-renewable), skill assignment, scheduling, and supplier selection. Applied and tested in collaboration with a Canadian construction firm specializing in building renovations, the proposed system demonstrates superior scalability compared to a standalone MILP approach—effectively managing large-scale scenarios involving up to 80 concurrent projects. Sensitivity analyses confirm the robustness of the heuristic-based model in enhancing project scheduling accuracy and resource coordination. The findings suggest that the DSS can significantly improve project completion times, workforce utilization, supplier engagement, and inventory control, offering a practical and impactful solution for CSC managers during the pre-construction phase.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".