Carbon emission-aware service allocation in Industrial Internet-enabled service-oriented manufacturing

Industrial Internet technologies have promoted the emergence and advancement of various smart service-oriented manufacturing (SOM) paradigms in the context of Industry 5.0. Existing research has considered energy consumption as an optimization objective to perform service allocation and balance the economic and environmental sustainability. Despite the significant progress, most approaches focused on reducing energy usage alone, without integrating the market mechanisms of carbon trading into the service allocation process. In contrast, carbon trading models not only account for the direct environmental impacts of energy consumption but also leverage market-based incentives to enable enterprises to dynamically balance economic performance and carbon emission during service allocation. To address the issue, this paper proposes a carbon emission-aware service allocation framework tailored for Industrial Internet-enabled SOM. By integrating dynamic carbon emission constraints into a multi-objective optimization process and employing multiple objective particle swarm optimization-non dominated sorting genetic algorithm-II (MOPSO-NSGA-II), the proposed approach strives to achieve an optimal balance among production efficiency, service quality, cost, and environmental impact. A case study is conducted to validate the superiority of the proposed approach in addressing carbon emission-aware service allocation problem.