Systematic data generation and test design for solution algorithms on the example of SALBPGen for assembly line balancing

Recently, the importance of correctly designed computational experiments for testing algorithms has been a subject of extended discussions. Whenever real-world data is lacking, generated data sets provide a substantive methodological tool for experiments. Focused research questions need to base on specialized, randomized and sufficiently large data sets, which are sampled from the population of interest. We integrate the generation of data sets into the process of scientific testing.

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