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

Disassembly is an integral part of maintenance, upgrade, and remanufacturing operations to recover end-of-use products. Optimization of disassembly sequences and the capability of robotic technology are crucial for managing the resource-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human-robot collaboration. The proposed model combines three attributes: disassembly cost, disassembleability, and safety, to find the optimal path for dismantling a product and assigning each disassembly operation among humans and robots. The multi-attribute utility function has been employed to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly and is assumed to be an uncertain parameter with a Beta probability density function; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the safety of human workers in the work environment. The optimization model identifies the best disassembly sequence and makes tradeoffs among multi-attributes. An example of a computer desktop illustrates how the proposed model works. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations between human and robot. A sensitivity analysis is conducted to show the model’s performance when changing the disassembly cost for the robot.