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This research considers a two-stage hybrid flowshop scheduling problem with challenging characteristics substantiated by the complexity of the problem in a collaborating company. Multiple (three) parallel machines are involved in the first stage and only one machine in the second stage. Stage 1 parallel machines are able to process multiple jobs simultaneously but the jobs must be sequentially setup one after another with the loading time depending on the processing time of the stage 2 machine. A blocking environment exists between the two stages with no intermediate buffer storage. In order to reduce the complexity, multiple simultaneous jobs are grouped into batches according to their similarities. Batches can then be considered as basic units for scheduling to determine which stage 1 machine and in what sequence batches are loaded. A genetic algorithm is used to obtain near-optimal schedules mainly by minimising the makespan. The proposed model and solution algorithm are applied to solve the problem in the collaborating company under a set of complicated rules and constraints. Comprehensive studies are conducted with real-life data. The results are consistent with the company's operational principles and are superior compared with the manual schedules. [ABSTRACT FROM AUTHOR]