<![CDATA[This paper addresses the complex multiobjective parallel machine scheduling problem within a bicycle painting department. The painting process is characterized by sequence-dependent setup times driven by frequent color changes and the critical need to align painting output with assembly shop requirements to prevent stock accumulation. The research develops a mathematical model with two primary objectives: minimizing the sequence mismatch between painting and assembly and minimizing total setup time. Unlike prior research, this study simultaneously addresses sequence-dependent setups caused by color changes, the management of multiple parallel paint lines within daily capacity limits, and the critical requirement that painting output matches the input sequence needed for assembly. Given the NP-hard nature of the problem, a Genetic Algorithm (GA) method is proposed to solve the problem. Other methods such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS), are used to compare with the GA. The methods are evaluated using real-world data from a bicycle company. Results indicate that VNS outperforms GA for the objective value. The objective value score of VNS is 91.23% compared to the Genetic Algorithm 86.72%. However, the GA runtimes are significantly faster, five times better than the VNS.]]>
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