Ant colony algorithms for minimizing costs in multi-mode resource constrained project scheduling problems with spatial constraints
Bibliographic record
Abstract
This paper addresses the problem of activity scheduling and operator assignment in workstations of aerospace assembly lines. The problem is modelled as a new variant of the Multi-Mode Resource Constrained Project Scheduling Problem (MRCPSP), which incorporates practical features from aerospace workstations in assembly lines. These workstations have a substantial number of activities to be scheduled within a given assembly cycle time. It introduces particularities which are not usually addressed such as considering additional workers for performing activities, different workers’ proficiency, and spatial limitations in work zones. The objective is to schedule the activities of an aerospace workstation, minimising the total labour cost, while satisfying the cycle time and the zone’s limitations. The problem is initially formulated by employing mixed-integer linear programming methods with mathematical modelling and solved using two different algorithms: an Ant Colony System (ACS) and a memetic ACS. Given the novelty of the problem presented, new sets of benchmark cases of different sizes for this problem are also proposed and solved. To assess the performance of the algorithms, the solutions for the small-sized instances are compared in terms of deviation with the results obtained by an optimisation modelling software. Further experimentation with the algorithms is carried out with medium and large instances, showing good performance and providing reasonably good results in realistic problems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".