Low-carbon multi-objective optimization of workshop based on electrical manufacturing IoT technology

In modern manufacturing, with the continuous expansion of electrical equipment manufacturing workshops, combining various technologies to build low-carbon scheduling models for workshops has important economic value and practical application significance. This study combines IoT technology and multi-objective optimization algorithms for lowcarbon scheduling in electrical equipment manufacturing workshops. The study first analyzes various data collection and communication technologies used in the electrical equipment manufacturing workshop, followed by the design of the entire production and processing platform. Finally, a low-carbon scheduling problem model for the job shop is established with the goal of minimizing carbon emissions, minimum processing costs, and maximum completion time, and improved using multiobjective algorithms. The research results showed that the optimal Cov index value of the proposed algorithm was about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$95 \%$</tex>, and the optimal HV index value was about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$63 \%$</tex>, which achieved the goal of minimizing carbon emissions optimization. In summary, the electrical manufacturing processing platform and low-carbon scheduling model built by the research institute can provide effective solutions for workshop scheduling problems and promote the sustainable development of manufacturing enterprises.