A Methodology of Task Allocation to Design a Human-Robot Assembly Line: Integration of DFA Ergonomics and Time-Cost Effectiveness Optimization

There are successful cases in lean manual assembly lines; however, in some cases, such as the ease of assembly in quicker cycle time, the designs are not satisfactory and must be transformed to semi-automation. This research studies human-robot task allocation when designing for semi-automation considering not only time-cost effectiveness as in the existing research but also assembly difficulty and ergonomic issues. A proposed methodology optimally determines what tasks should be performed by humans or robots, at which station, and in what sequence. A multi-objective linear programming (MOLP) model is proposed to simultaneously minimize total operating cost, cycle time, and ergonomic difficulty. Solving the model has two approaches: with and without optimal weights. The methodology is applied to a Lego-car assembly line. To illustrate the benefits of the proposed MOLP, a comparison between it and three single-objective models is made. Results show that the optimal-weight MOLP yields a better performance (a shorter cycle time, a lower cost, and especially, a significant ergonomic improvement) when compared to the other MOLP and single-objective models.

Automated summary

The research proposes a methodology for human-robot task allocation in semi-automation design, considering assembly difficulty and ergonomic issues in addition to cost and time. By using a multi-objective linear programming model, tasks are optimized for humans or robots at different stations and sequences, leading to improved performance in terms of cycle time, cost, and ergonomic improvement. The optimal-weight MOLP outperforms other models in the study, demonstrating significant benefits in assembly line optimization.