Abstract
The strategic reduction of power consumption in electrical machines, particularly within manufacturing networks, is critical for achieving energy efficiency and sustainability. Bio-inspired algorithms have demonstrated remarkable potential for optimization, with their adaptability to specific problem domains being a key factor in their success. This study employs discrete optimization algorithms tailored for scheduling in an electrical manufacturing network modeled as a hybrid flow shop, where energy efficiency is paramount. Experimental evaluations were conducted on a customized hybrid flow shop benchmark comprising approximately 45 cases and 900 instances, utilizing a random distribution of hourly energy consumption and a specific tariff structure for monthly usage. The findings demonstrate the efficacy of the proposed discretized metaheuristics in significantly reducing power consumption costs while maintaining a minimized total makespan. This work highlights the potential of bio-inspired algorithms for green hybrid flow shop scheduling within electrical networks, contributing to enhanced operational efficiency, sustainability, and energy conservation in modern manufacturing environments.
Published Version
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