Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations

In the remanufacturing industries, automated disassembly becomes one of the most promising solution in achieving economic benefit. Robotic disassembly line balancing is a key problem that enables automated disassembly to be implemented at industrial scale. This paper focuses on evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. In each workstation, multiple skilled robots perform different tasks belonging to the different end-of-life products or subassemblies simultaneously. Based on the transformed AND/OR graph and parallel disassembly, a mathematical programming model is proposed to minimize the cycle time, the total energy consumption, the peak workstation energy consumption, and the number of robots being used simultaneously. Furthermore, a problem knowledge-leveraging evolutionary algorithm, including encoding/decoding scheme, initialization approach and problem-specific variation operators, is developed to deal with the above problem. Comprehensive experiments are conducted based on 8 product models and 63 problem instances generated in this study. In particular, a comparative study of our proposed algorithm and 5 representative evolutionary algorithms selected from the 3 classes of approaches of dealing with many-objective problems are provided. Then some insights with respect to the design of evolutionary algorithms for our problem are gained from the investigation.