A Two-Level Optimization Approach For Engineer-To-Order Project Scheduling*
Bibliographic record
Abstract
This paper presents a new formulation of the flexible job-shop scheduling problem with outsourcing options adapted for Engineer-To-Order (ETO) products. Our formulation enables modeling complex product structures with flexible precedence relations between elements and operations. Having a significant role in the ETO context, the specificity of non-physical operations (design and engineering) is taken into account. Indeed, non-physical operations are subject to a validation stage, can be iterated in case of non-validation, and are executed once for several identical elements. The proposed approach is governed by a new ETO strategy to overcome the impact of the design uncertainty and element cancellations (time and financial wastes). First, the production and purchase of the most uncertain elements are delayed at the latest while their design is validated early. Besides, in the presence of similar elements, an element can be saved when cancelled by being used as a sub-part of another one. The proposed approach sequentially solves two mathematical models. The first model aims to minimise the makespan and outsourcing costs. The second model maximizes the solution robustness and the ability of saving elements while being governed by the completion time and project cost, output of the first model. The obtained results and a comparative study show the efficiency and robustness of the proposal.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".