hybrid Genetic-ADAM Optimizer to Improve Disassembly Line Balancing Problem

Modern products are designed to meet the needs of customized and short-lived products, after a short period of time, these products become waste products, these waste products are accumulating at an exponential rate, resulting in environmental degradation as a result of pollution, in order to keep this environment clean, it is necessary to have disassembly lines for those waste products, so in this research a Hybrid Genetic ADAM optimizer Algorithm was proposed to facilitate the disassembly operations at work stations placed in the disassembly lines, the proposed algorithm used to solve multi-objective disassembly line balancing problem, and Pareto optimal solution was used to determine non inferior solutions from a population. The trend of results reveal 6.1 percent a reduction in disassembly workstations, reduction of at least 0.05 percent of the idle time, reducing to minimum run time 1.5 percent comparing proposed and other meta heuristics algorithms. Furthermore, the plans of disassembly data are created to be valuable regarding trace ability and disassembly improvement processes in the future.