Cost-efficient design and optimization of robotic assembly lines using a non-dominated sorting genetic algorithm framework

Abstract

The total cost of assembly is a critical factor in robotic assembly line balancing, as it encompasses all the costs associated with the assembly line, including initial costs, setup, maintenance, and energy cost. This study introduces a different approach to the robotic assembly line balancing problem, with a dual focus on minimizing both cycle time and overall assembly costs. The effectiveness of the proposed approach is validated through three case study problems taken from the literature and results are compared to traditional assembly allocation methods. For case study 1, 89.4% (42 out of 47) of the solutions achieved a lower total cost, and 34% (16 out of 47) of the solutions utilized fewer workstations; and for case study 2, 96.4% (108 out of 112) of the solutions achieved a lower total cost, and 58.9% (66 out of 112) of the solutions utilized fewer workstations for the same cycle time. These results demonstrate a significant savings in cost and a notable improvement in workstation efficiency for a substantial portion of the solutions. This comprehensive approach allows an effective resource allocation, reduces inefficiencies, and enhances the overall cost-effectiveness and performance of the robotic assembly line. It also supports decision-makers in selecting more sustainable and economically viable assembly line solutions that optimize both productivity and energy efficiency.