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Collaborative optimization for energy saving and service composition in multi-granularity heavy-duty equipment cloud manufacturing environment

Efficient service scheduling is an important technique supporting collaborative manufacturing platforms such as cloud manufacturing. To achieve a more efficient task execution, heavy-duty equipment manufacturing, an important field of cloud manufacturing, must be explored beyond parameters of cost and time. The manufacturing service composition problem of heavy-duty equipment has the characteristics of task complexity, high process energy consumption, and multi-granularity nature of service (MGNoS). In the manufacturing process of heavy-duty equipment, the energy consumption of required logistics accounts for 30% the total energy consumption. However, to date, research has investigated the problem almost always from the task level, and MGNoS has received little attentions, which may lead to redundant energy consumption in logistics during manufacturing execution. In this paper, the problem of manufacturing service scheduling with integrating energy saving and service composition in cloud manufacturing is considered. Based on the mathematical description, a cross-granularity task chain reconfiguration algorithm is presented for mitigating the adverse effects of MGNoS and developing the adaptive non-dominated sorting genetic algorithm Ⅲ for solving the service composition scheme to generate optimal scheduling solutions. The effectiveness and efficiency performances of typical optimization algorithms are compared with the proposed approach. The results show that the proposed method achieves significant energy savings for all tasks in different scenarios.