Multi-objective Optimisation of a Family of Industrial Robots

  • Johan Ölvander
  • Mehdi Tarkian
  • Xiaolong Feng


Product family design is a well recognised method to address the demands of mass customisation. A potential drawback of product families is that the performance of individual members are reduced because of the constraints added by the common platform, i.e., parts and components need to be shared by other family members. This chapter presents a framework where the product family design problem is stated as a multi-objective optimisation problem and where multi-objective evolutionary algorithms are applied to solve the problem. The outcome is a Pareto-optimal front that visualises the trade-off between the degree of commonality (e.g., number of shared components) and performance of individual family members. The design application is a family of industrial robots. An industrial robot is a mechatronic system that comprises a mechanical structure (i.e., a series of mechanical links), drive-train components (including motors and gears), electrical power units and control software for motion planning and control.


Pareto Front Product Family Robot Manipulator Industrial Robot Pareto Frontier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Management and EngineeringLinköping UniversityLinköpingSweden
  2. 2.ABB Corporate ResearchVästeråsSweden

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