As climate challenges intensify, ecological objectives are gaining importance alongside traditional objectives in distributed scheduling, giving rise to distributed green scheduling problems. However, current models and objectives fail to capture key characteristics of geographically distributed manufacturing systems, particularly the emission intensity of electricity generation and the distribution of goods. Since the environmental impact of electricity consumption varies with local emission factors, they are critical in distributed permutation flowshop scheduling problems. Further, the validity of ecological optimization can be compromised, as energy savings may be offset by increased transportation-related emissions. Based on an experimental analysis calibrated to real-world European production networks and including makespan as an economic objective, we find that optimizing total energy consumption results in an average hypervolume RPD of 42.12%, questioning its validity as an indicator of environmental performance in distributed scheduling. Moreover, focusing solely on production-related emissions still results in an average deviation of 26.13%, highlighting the bias caused by neglecting the distribution stage — an effect that becomes more pronounced with increasing product weight. To further enhance real-world applicability, we assess the impact of eligibility constraints — arising from limited redundancy in tools and raw materials — on the potential to minimize both makespan and carbon emissions, and propose distance- and emission-aware strategies for factory qualification. Finally, the problem is solved using a novel parameter-less iterated greedy algorithm that incorporates problem-specific knowledge into speed factor adjustment, removes the need for parameter tuning, and demonstrates strong solution quality in extensive computational experiments.
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