What does this research mean for the field?

Integrating collaborative robots for large-part picking in assembly systems simultaneously improves productivity and operator ergonomics, and its economic feasibility can be optimized through an integrated framework considering task characteristics, storage, and ergonomic exposure. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Determine whether collaborative robots can support large-part picking in human-robot collaboration assembly systems to jointly enhance productivity, physical ergonomics, and economic feasibility.

September 5, 2026The International Journal of Advanced Manufacturing TechnologyOpen Access

Can collaborative robotics help in large parts picking for assembly? An ergonomics/economics model

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Authors

MFMaurizio FaccioUniversity of Padua IGIrene GranataUniversity of Padua RMRiccardo MintoUniversity of Padua

Discussion

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Implication

Optimization modeling study demonstrates improved cycle times and reduced ergonomic strain in collaborative assembly workstations, highlighting the viability of collaborative robots.

Key Points

Determine whether collaborative robots can support large-part picking in human-robot collaboration assembly systems to jointly enhance productivity, physical ergonomics, and economic feasibility.
Formulated an offline decision-support framework incorporating storage- and attribute-dependent Picking-and-Placing Time estimation.
Applied a Mixed-Integer Linear Programming model to allocate 26 assembly tasks between two human operators and one collaborative robot.
Assessed postural strain using time-weighted Rapid Entire Body Assessment and determined financial viability with iso-payback economic modeling.
The makespan-oriented configuration decreased assembly cycle time from 553 s in the human-only baseline to 493 s, an 11% reduction.
The selected bi-objective configuration achieved a 507 s cycle time while reducing time-weighted REBA scores from 2.45 to 1.91 for Operator 1 and from 2.05 to 0.73 for Operator 2.
Economic analysis demonstrated that productivity gains primarily ensure viability under low margins, whereas monetizing ergonomic benefits broadens the acceptable payback window.

Cite This Study

Faccio et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4726b95aff0620ec237 https://doi.org/10.1007/s00170-026-18980-z

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September 5, 2026The International Journal of Advanced Manufacturing TechnologyOpen Access

Can collaborative robotics help in large parts picking for assembly? An ergonomics/economics model

View Full Paper

Ask AI

Bookmark

Authors

MFMaurizio FaccioUniversity of Padua IGIrene GranataUniversity of Padua RMRiccardo MintoUniversity of Padua

Discussion

Member takes

Implication

Optimization modeling study demonstrates improved cycle times and reduced ergonomic strain in collaborative assembly workstations, highlighting the viability of collaborative robots.

Key Points

Determine whether collaborative robots can support large-part picking in human-robot collaboration assembly systems to jointly enhance productivity, physical ergonomics, and economic feasibility.
Formulated an offline decision-support framework incorporating storage- and attribute-dependent Picking-and-Placing Time estimation.
Applied a Mixed-Integer Linear Programming model to allocate 26 assembly tasks between two human operators and one collaborative robot.
Assessed postural strain using time-weighted Rapid Entire Body Assessment and determined financial viability with iso-payback economic modeling.
The makespan-oriented configuration decreased assembly cycle time from 553 s in the human-only baseline to 493 s, an 11% reduction.
The selected bi-objective configuration achieved a 507 s cycle time while reducing time-weighted REBA scores from 2.45 to 1.91 for Operator 1 and from 2.05 to 0.73 for Operator 2.
Economic analysis demonstrated that productivity gains primarily ensure viability under low margins, whereas monetizing ergonomic benefits broadens the acceptable payback window.

Cite This Study

Faccio et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4726b95aff0620ec237 https://doi.org/10.1007/s00170-026-18980-z

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