Soon, a new generation of Collaborative Robots embodying Human-Robot Teams (HRTs) is expected to be more widely adopted in manufacturing. The adoption of this technology requires evaluating the overall performance achieved by an HRT for a given production workflow. We study this performance by solving the underlying scheduling problem under different production settings. We formulate the problem as a Multimode Multiprocessor Task Scheduling Problem, where tasks may be executed by two different types of resources (humans and robots), or by both simultaneously. Two algorithms are proposed to solve the problem - a Constraint Programming model and a Genetic Algorithm. We also devise a new lower bound for benchmarking the methods. Computational experiments are conducted on a large set of instances generated to represent a variety of HRT production settings. General instances for the problem are also considered. The proposed methods outperform algorithms found in the literature for similar problems. For the HRT instances, we find optimal solutions for a considerable number of instances, and tight gaps to lower bounds when optimal solutions are unknown. Moreover, we derive some insights on the improvement obtained if tasks can be executed simultaneously by the HRT. The experiments suggest that collaborative tasks reduce the total work time, especially in settings with numerous precedence constraints and low robot eligibility. These results indicate that the possibility of collaborative work can shorten cycle time, which may motivate future investment in this new technology.

很快，将体现出由人类机器人团队（HRT）组成的新一代协作机器人。这项技术的采用要求评估HRT在给定的生产工作流程中实现的总体性能。我们通过解决不同生产设置下的基础调度问题来研究此性能。我们将该问题表述为“多模式多处理器任务计划问题”，其中任务可以由两种不同类型的资源（人和机器人）执行，也可以同时由两种资源执行。提出了两种算法来解决该问题：约束规划模型和遗传算法。我们还为基准测试方法设计了一个新的下限。在大量代表各种HRT生产设置的实例上进行了计算实验。还考虑了该问题的一般实例。提出的方法优于文献中针对类似问题的算法。对于HRT实例，我们找到了大量实例的最佳解决方案，并且在未知最佳解决方案的情况下，可以找到较低的界限。此外，如果HRT可以同时执行任务，我们将获得一些改进方面的见解。实验表明，协作任务可以减少总的工作时间，尤其是在具有许多优先级约束且机器人资格较低的环境中。这些结果表明，开展协作工作的可能性可以缩短周期时间，这可能会激发未来对该新技术的投资。