Heterogeneous Multi-Robot Task Scheduling Heuristics for Garment Mass Customization

Industrial environments that rely on Mass Customization are characterized by a high variety of product models and reduced batch sizes, demanding prompt adaptation of resources to a new product model. In such environment, it is important to schedule tasks that require manual procedures with different levels of complexity and repetitiveness. In a garment mass customization scenario, task scheduling needs to take into consideration the dependency of the tasks, meaning that in order to initiate a certain task, materials from previous tasks may be required. In order to carry out a smooth scheduling process within a garment mass customization factory, not only the tasks but also the transportation of materials to perform such tasks need to be scheduled to static and mobile robots, respectively. To tackle this problem, we propose a set of heuristics that are able to schedule both the task work and transportation of materials. We analyze these heuristics theoretically with respect to computational complexity. Subsequently, the performance of each algorithm is evaluated using a synthetic testset. The comparative analysis shows that the extended algorithms have close results among themselves, whereas for the heuristics, Minimum Transportation Cost (MTC) outperforms all of the other algorithms. Moreover, the combination of Predict Earliest Finish Time (PEFT) and MTC is more efficient compared to other algorithm combinations.