The optimization of job-shop scheduling problems, such as those found in the semiconductor industry, is an NP-hard challenge. Research has demonstrated that agent-based modeling of production plants can effectively plan tasks, maximize productivity (utilization and timeliness), and minimize production delays. This bottom-up optimization approach especially addresses the computational challenges associated with traditional, centrally calculated optimization methods. In this context, we focus on a dynamic semiconductor production plant, modeling both machines and products as agents. We propose two variants of the Artificial Bee Colony algorithm for scheduling from the bottom up. Variant (1) emphasizes decentralization and batch processing to enhance production speed, while Variant (2) aims to predict production times to reduce queuing delays, achieving reductions in Flow Factor and Tardiness across most lots, though with a slight increase in Makespan for a few cases. Both algorithmic variants are evaluated within the SwarmFabSim framework, which is designed in NetLogo, specifically addressing the job-shop scheduling problem in the semiconductor industry. Through this study, we analyze the effectiveness of the bottom-up algorithms that rely on low-effort local calculations.