Safety-driven optimisation of human–robot collaborative assembly line balancing
Bibliographic record
Abstract
In the dynamic field of advanced manufacturing, integrating collaborative robots (cobots) into assembly lines has emerged as a transformative approach to improve efficiency and human job quality. This paper addresses the safety challenge of human–robot collaboration (HRC) in the context of an assembly line balancing problem (ALBP) aimed at minimising cycle time. A constraint programming (CP)-based model optimally assigns cobots to workstations, allocates assembly tasks between humans and cobots, and sequence them on a single-model line. The CP model includes a novel workstation zoning policy to eliminate the medium and high-risk parallel tasks in a collaborative workstation. Numerical examples illustrate the impact of the proposed policy. Comparing the optimal results in the presence and absence of zone constraints in HRC-ALBP reveals improved safety at the cost of a minor increase in cycle time. This contributes to bridging the gap between HRC safety and operational goal. With minor modifications, a second version of the model minimises the number of cobots used, providing further insight into the optimisation of cobot integration. By considering different objective functions, safety-oriented constraints, and managerial insights, this work contributes to the creation of a decision-support tool that aligns with the human-cantered principles of Industry 5.0.
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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) | 0.004 | 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".