Energy consumption aware method for cloud manufacturing service selection and scheduling optimization
Cloud Manufacturing Service Selection and Scheduling(CMSSS)problem has attracted much attention in optimizing resource allocation and meeting user requirements.However,most existing methods pay insufficient con-sideration to the preheating process of manufacturing equipment,resulted in wasted energy.To reduce manufacturing energy consumption and guarantee Quality of Service(QoS),a multi-objective optimization model for CMSSS was established,the preheating energy consumption of manufacturing service equipment was quantified by a task cohesion degree model,and an Energy Consumption Aware Method(ECAM)for CMSSS optimization was pro-posed.The method selected a composite service for the task according to QoS metrics,and scheduled subtasks to meet the highest cohesion degree in the idle time of the manufacturing service according to the occupation,so as to reduce the preheating energy consumption of the manufacturing equipment.The results showed that ECAM had su-perior fitness to the previous Feasible Schedule Generation Schema(FSGS)under 6 weights evaluation metrics.In cloud manufacturing scenarios with preheating process,ECAM achieved basically the same QoS satisfaction and bet-ter energy economy as FSGS.
cloud manufacturingservice selection and schedulingtask cohesionpreheating energyevolutionary al-gorithm
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