Multiobjective Multiverse Optimizer for Multirobotic U-Shaped Disassembly Line Balancing Problems

The development of technology accelerates the upgrade of products, which results in a significant number of obsolete products. This research aims to solve the multirobotic multiproduct U-shaped disassembly line balancing problem (M2UDP), in which different products are disassembled on a U-shaped model in a preset cycle time and assigned tasks to robots in each workstation reasonably. A linear mixed-integer model is established to maximize disassembly profit and minimize carbon emissions. An improved multiobjective multiverse optimizer (IMMO) that utilizes the sigmoid activation function in neural networks is proposed to find the optimal plan for the model. The improved algorithm is verified via a set of real-life instances and compared with three classical multiobjective optimization algorithms. The experimental results show that the proposed IMMO performs better than those peers in solving the M2UDP problems.