Optimized Task Scheduling for Human-Cobot Collaboration Based on Value-Added Ratio

The integration of collaborative robots (cobots) in the assembly line balancing problem (ALBP) represents a challenging opportunity to perform strategic task assignments to workstations targeting both assembly line efficiency and worker satisfaction. Cobots are designed to accomplish the progression of repetitive or hazardous tasks, allowing workers to dedicate more attention to valuable assembly activities that require non-replicable skills and human dexterity. Deploying human-robot collaboration (HRC) in ALBP often aims at increasing system performance as its primary objective; however, multi-objective models have started to spread in literature considering both economic, social, and sustainable targets, demonstrating compliance with Environmental, Social, and Governance (ESG) paradigm and Industry 5.0 principles. This study proposes a bi-objective mixed-integer nonlinear programming (MINLP) mathematical model to simultaneously minimize cycle time and the percentage of non-value-added ratio. In particular, the algorithm developed targets the workstation that exhibits the greatest cycle time in the ALBP solution, thereby constraining the productivity of the assembly line. Maximizing value-added task assignments to workers does not only imply reducing the strenuous workload and hazardous task progression but also favoring the progression of assembly activities that can increase motivation and morale of workers due to the high skills and non-replicable competences required for their accomplishment. The proposed model is applied to a numerical test case on an experimental dataset to provide preliminary results for the HRC-ALBP.