Balancing and scheduling human-robot collaborative assembly lines with heterogeneous robots and limited resources: Constraint programming approach and fruit fly optimization algorithm - Zheng, Chenyu; Li, Zixiang; Janardhanan, Mukund; Zhang, Zikai; Zhang, Liping (Computers & industrial engineering, 2025)
•Balancing and scheduling of assembly lines with heterogeneous collaborative robots is considered.•Constraint programming approach is formulated to solve the considered problem.•Constraint programming is developed in decoding to obtain the optimal scheduling scheme.•Lower bound, upper bound are developed for the constraint programming.•Fruit fly optimization algorithm with three improvements is developed. Collaborative robots are increasingly utilized to assist the human workers to assemble tasks or complete the assembly tasks solely in assembly lines. This study considers the human-robot collaborative assembly lines with heterogeneous collaborative robots and limited resources to optimize cycle time where human workers and collaborative robots can operate the different tasks in parallel. A constraint programming model is formulated that was able to achieve the optimal solutions for small-sized instances. An improved fruit fly optimization algorithm is developed to tackle the large-sized instances. The proposed algorithm proposes two vectors for encoding, where task assignment vector tackles task allocation sub-problem and process alternative vector tackles process alternative allocation sub-problem. This algorithm utilizes a decoding procedure with a constraint programming approach to achieve an optimal scheduling scheme of the station with human worker and collaborative robot. The lower bound and upper bound of completion time of single station and the earliest and latest processing time of tasks are added to the constraint programming model to speed up the search process. Meanwhile, improved fruit fly optimization algorithm utilizes the improved olfactory phase, improved visual phase and restart phase to accelerate the evolution of the whole swarm and avoid being trapped in local optimum. Computational study demonstrates that constraint programming approach outperforms the current mixed integer programming approach in objective value and solution time. The decoding procedure with constraint programming outperforms the current decoding procedure with mixed integer programming. Comparative study demonstrates that the proposed method outperforms the original fruit fly optimization algorithm and achieves promising performance in comparison with other methods. Finally, the proposed method is applied on scheduling of a gear box assembly line.