Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human-Robot Collaboration

Disassembly is an essential step for remanufacturing end-of-life (EOL) products. Optimiza-tion of disassembly sequences and the utilization of robotic technology could alleviate the labor-intensive nature of dismantling operations. This st u d y proposes an optimization framework for disassembly sequence planning under uncertainty considering human- robot collaboration. The proposed framework combines three attributes: disassembly cost, safety, and complexity of disassembly, namely disassembleability, to identify the optimal disassembly path and allocate operations between human and robot. A multi-attribute ut i l i t y function is used to address uncertainty and make a tradeof f among multiple attributes. The disassembly t i m e reflects the cost of disassembly which is assumed to be an uncertain parameter with a Beta distribution; the disassembleability evaluates the feasibil-ity of conducting operations by robot; finally, the safety index ensures the protection of human workers in the work environment. An example of dismantling a desktop computer is used to show the application. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allo-cating disassembly operations among human and robot. A sensitivity analysis is conducted to show the model's performance when changing the disassembly cost for the robot.

Automated summary

This study proposes an optimization framework for planning disassembly sequences under uncertainty, considering human-robot collaboration. The framework combines disassembly cost, safety, and complexity to identify the optimal disassembly path and allocate operations between human and robot.