Differential evolution variants to schedule flexible assembly lines

Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based algorithm are employed to schedule flexible assembly lines (FAL). The performance of the assembly line is optimized based on two performance criteria, namely the weighted sum of Earliness/Tardiness penalties and the balance of the assembly line. Different variants of the Bi-level differential evolution (BiDE) algorithms are developed to study the effects of three FAL problems. The parameters of BiDE algorithm for FAL problems are fine-tuned. The performance of the BiDE algorithm is evaluated using the datasets and the Bi-level Genetic Algorithm (BiGA) available in the literature. The experimental results show that the proposed differential evolution-based algorithm outperforms BiGA in terms of mean best fitness.