Solving Resource Constrained Project Scheduling Problems (RCPSP) with Remanufacturing
Bibliographic record
Abstract
Scheduling is one of the crucial issues in the project planning phase. Completing the project in the desired duration with the available resources with minimum cost is a big challenge for project managers. \nIn the recent decades, several approaches have been proposed to deal with the resource constraints in scheduling. It can create a serious bottleneck and drastically change the flow of the activities. Moreover, resource constrains can change the project duration in crashing the project even if the activity (which creates the bottleneck) is not on the critical path. \nTo address this issue, a new approach for Resource Constrained Project Scheduling (RCPS) is proposed when the remanufacturing option for some activities is available in order to crash the project. In this research, first a mathematical model for RCPS is presented. Then, a new algorithm is proposed to shorten the project duration by activating remanufacturing line (if possible) or paying the crash cost. The proposed algorithm is implemented in MATLAB and some computational experiments have been done to demonstrate the effectiveness and sensitivity of the proposed procedures. The algorithm is also validated on a practical case study which is a manufacturing industry in the northern Ontario.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".