Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures

Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) efficiency. Most of the current research on line balancing problems focuses decomposing one product. More than can be disassembled concurrently, further improve Moreover, uncertainty such as depreciation EOL products, may result failure. In this research, a stochastic multi-product robotic model is established using an AND/OR graph. It takes precedence relationship, cycle constraint, and failure into consideration to maximize profit minimize energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with simulation proposed solve problem. Furthermore, by conducting experiments some real cases comparing four state-of-the-art algorithms, i.e., discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic II, evolutionary based decomposition, paper validates its excellent performance solving concerned