Cloud Manufacturing Service Selection and Optimization Scheduling Method with Time-of-Use Electricity Price Constraints

Cloud manufacturing service selection and scheduling has received extensive attention in optimizing resource allocation and meeting user needs. Electricity consumption accompanies the entire life cycle of cloud manufacturing services. However, few existing studies on the selection and scheduling of cloud manufacturing services have considered the constraint of TOU. In order to reduce power cost and ensure service quality , a multi-objective optimization model for cloud manufacturing service selection and scheduling under TOU is established. An improved multi-objective Seagull optimization algorithm (MOISOA) is proposed to solve the problem of cloud manufacturing service selection and scheduling under time-of-use tariff (TOU). Experimental results show that MOISOA algorithm can obtain better solutions than many classical multi-objective algorithms.