A Fuzzy Inference System-Based Hybrid Assignment Method for Cobot Assignment Problem

Abstract

To address the complexity of a cobot assignment problem in smart manufacturing, this research has pioneered a new hybrid assignment method integrating the fuzzy inference system (FIS) into both multi-objective assignment and linear assignment models. It focuses on the critical tasks of assigning robots, jobs, and workers to create efficient cobot workstations, covering three core activities: robot-to-job, job-to-robot, and worker-to-robot-job assignments. An FIS-based multi-objective assignment model is utilized in the initial step to form effective sets of a single robot and jobs. The model also allows users to adjust the level of negative deviation from the target to enhance the satisfaction of the decision-maker. In the final step, workers are assigned and the cobot workstations are concurrently created using a FIS-based linear assignment model. To illustrate the method's efficacy, we provided a practical example that demonstrates its performance in action for industrial applications.