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Balancing collaborative human–robot assembly lines to optimise cycle time and ergonomic risk

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  • Kathryn E. Stecke
  • Mahdi Mokhtarzadeh

Abstract

Human–robot collaboration can enhance productivity of production lines and reduce human ergonomic risk. The numbers and types of robots and stations in which robots are allocated need to be determined. Operations should be scheduled carefully when a human and robot work on a part in a station to obtain a feasible operation allocation with the highest efficiency and lowest ergonomic risk. A mixed-integer linear programming model, constraint programming model, and Benders decomposition algorithm were developed to analyse advantages of collaborative robots in assembly lines. An energy expenditure method was used to evaluate ergonomic risk. By scheduling and balancing collaborative human–robot assembly lines, operational advantages and scheduling constraints from human–robot collaboration were studied when immobile and mobile robots are used. Regression lines were developed that can help managers determine how many and what types of robots are best for a line and what the impact of robot mobility on robot and line performance can be. The best configuration for equipping a line with collaborative robots is when (number of robots)/(number of stations) is near .7 and about 37% of robots are mobile. Robots can be efficiently used in lines with both a small and large number of passive resources and in simple and mixed-model lines.

Suggested Citation

  • Kathryn E. Stecke & Mahdi Mokhtarzadeh, 2022. "Balancing collaborative human–robot assembly lines to optimise cycle time and ergonomic risk," International Journal of Production Research, Taylor & Francis Journals, vol. 60(1), pages 25-47, January.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:1:p:25-47
    DOI: 10.1080/00207543.2021.1989077
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    Cited by:

    1. Mao, Zhaofang & Sun, Yiting & Fang, Kan & Huang, Dian & Zhang, Jiaxin, 2024. "Balancing and scheduling of assembly line with multi-type collaborative robots," International Journal of Production Economics, Elsevier, vol. 271(C).

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