Optimized Task Scheduling for Human-Cobot Collaboration Based on Value-Added Ratio
Bibliographic record
Abstract
The integration of collaborative robots (cobots) in the assembly line balancing problem (ALBP) represents a challenging opportunity to perform strategic task assignments to workstations targeting both assembly line efficiency and worker satisfaction. Cobots are designed to accomplish the progression of repetitive or hazardous tasks, allowing workers to dedicate more attention to valuable assembly activities that require non-replicable skills and human dexterity. Deploying human-robot collaboration (HRC) in ALBP often aims at increasing system performance as its primary objective; however, multi-objective models have started to spread in literature considering both economic, social, and sustainable targets, demonstrating compliance with Environmental, Social, and Governance (ESG) paradigm and Industry 5.0 principles. This study proposes a bi-objective mixed-integer nonlinear programming (MINLP) mathematical model to simultaneously minimize cycle time and the percentage of non-value-added ratio. In particular, the algorithm developed targets the workstation that exhibits the greatest cycle time in the ALBP solution, thereby constraining the productivity of the assembly line. Maximizing value-added task assignments to workers does not only imply reducing the strenuous workload and hazardous task progression but also favoring the progression of assembly activities that can increase motivation and morale of workers due to the high skills and non-replicable competences required for their accomplishment. The proposed model is applied to a numerical test case on an experimental dataset to provide preliminary results for the HRC-ALBP.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".