Superstructure-based optimization of robotic assembly lines

Approaches to solve the assembly line balancing problem and its variations have been examined in research for several decades. Hybrid problems have replaced individual problems as the focus of consideration. Nevertheless, most of the theoretical models have not been applied in industry yet. This paper aims to close the research gap regarding the application of optimization strategies to production planning for fully automated robotic assembly lines. Therefore, we present a solution for solving a real-world hybrid problem that searches for the most cost-effective system. A balancing problem is solved for systems in which several robots can work together in parallel on fixtures or stationary joining units. The practical application includes the fact that the sequence of tasks is not given in a precedence graph, but instead, there is a sequence of processes that are implemented by several tasks. The planning of the task execution, optionally using different resources and in parallel, is part of the scheduling and equipment selection problem. We present a general domain model and a superstructure for robotic assembly lines and, based on this, an MILP formulation for the hybrid problem. Solutions for different cycle times were calculated for a real-world example and the results of this optimization are discussed and evaluated. • Solving a hybrid problem in robotic assembly line balancing. • Simultaneous optimization of line balancing, scheduling and equipment selection. • Domain model and superstructure for robotic assembly lines lead to a mathematical (MILP) formulation. • Generating a precedence graph for robotic assembly lines from process information. • Application of method to a real-world example and optimizing a cost-effective system.