Abstract
The development of technology accelerates the upgrade of products, which results in a significant number of obsolete products. This research aims to solve the Multi-robotic Multi-product U-shaped Disassembly-line-balancing Problem (M2UDP), in which different products are disassembled on a U-shaped model in a pre-set 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 multi-objective multi-verse 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 multi-objective optimization algorithms. The experimental results show that the proposed IMMO performs better than those peers in solving the M2UDP problems.
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