Optimization of Assembly Scheduling Based on an Improved Adaptive Genetic Algorithm

Optimization of assembly scheduling is the key to Just-in-time production. To optimize assembly scheduling for reducing the assembly time, an improved adaptive genetic algorithm based on neighborhood search is proposed. Two-layer encoding is used to represent the assembly scheduling. Optimizing the selection of parent chromosomes by calculating the gap between two chromosomes with Inertial Mass and Euclidean Distance in gravitational search algorithm. At the same time, the genetic algorithm and the variable neighborhood algorithm based on N6 neighborhood structure are integrated to achieve the effective optimization of the total assembly scheduling. The algorithm is tested and analyzed based on the benchmark example, and the optimization results are compared with an actual assembly scheduling case to verify the effectiveness and feasibility of the algorithm, which validates the effect of optimizing assembly scheduling.