The manufacturing industry is increasingly demanding flexible manufacturing and existing manufacturing methods with fixed equipment do not meet this requirement. The free spot assembly system is an ultra-flexible method that responds to this demand, enabling spatiotemporal free assembly by conveying all necessary resources with automated guided vehicles (AGVs). Although some studies have proposed free spot assembly, free spot assembly feasibility for assembling heavy objects, such as machine tools, by aligning them at high precision has not been verified. Work hour shifts, differences in worker skill levels, and cooperative work with robots have also not been considered in free spot assembly scheduling. This paper presents elemental technologies for realizing a free spot assembly system, with a scheduling method where a genetic algorithm is supported by dispatching rules with six genes. The computational results reveal the effectiveness of the proposed algorithm.

In the scheduling of assembly lines with human-robot collaboration, variations in workload caused by differences in the available working hours of workers and robots must be minimized. A scheduling method that considers buffers shared by automated guided vehicles and cooperative assembly by multiple workers is proposed herein. In particular, cooperative work requires an assembly schedule that minimizes the make span and satisfies the delivery date, while accounting for the possibility of work partitioning, the number of workers, as well as their available time slots and skills. Hence, it is difficult to obtain an exact optimal solution within a reasonable computation time using existing methods such as mathematical programming. Heuristic or metaheuristic approaches are effective for solving this problem. However, these approaches are not suitable for cooperative assembly by multiple workers. Therefore, a genetic algorithm supported by dispatching rules with four genes is proposed. Computational experiments are conducted based on multiple worker skills. The results showed that when the worker skills are the same, the genetic representation of the job name and part processing order is effective, whereas when the worker skills are different, the genetic representation of the cooperative process with the worker for each operation is effective.