Production scheduling for human–robot collaborative assembly workstations under constraints of ergonomic fatigue and simultaneous cooperation

Human–robot collaboration (HRC) is a key enabler of human-centric manufacturing, achieved through cooperation between human operators and collaborative robots. HRC can be classified into three developmental phases: coexistence, sequential collaboration, and simultaneous cooperation. To address ergonomic fatigue and simultaneous cooperation (HRCAW-ES) constraints, this study introduces a novel scheduling model that integrates sequential collaboration and simultaneous cooperation, focusing on production scheduling in shared HRC assembly workstations involving one human operator and one collaborative robot. This setting accounts for key operational constraints, including operation precedence and assembly relationships, human task eligibility based on ergonomic risk factors, ergonomic fatigue accumulation and recovery following established models, sequence-dependent setup for end-effector switching on a collaborative robot, and simultaneous cooperation between the two collaborators. A mathematical model was developed to formulate an adaptive variable neighbourhood search (AVNS) algorithm and a disjunctive graph representation was employed to analyse the structural characteristics of the HRCAW-ES. An ablation study performed using both linear and nonlinear fatigue models revealed the superior performance of the proposed AVNS algorithm compared to the control group across various scenarios involving varying cooperation ratio and fatigue levels. This experiment includes results obtained using parameters collected from the small-product packaging and cable-assembly processes. Emphasis was placed on examining the impacts of ergonomic limitations and simultaneous cooperation within the scheduling framework. The proposed method generates high-quality, feasible schedules to address the complexity introduced by ergonomic constraints and cooperative requirements. The method may be extendable to a wide range of assembling processes where full automation is infeasible.