Abstract
Service composition optimization is one of the core issues of cloud manufacturing research. At present, relevant researches mostly focus on single-task service composition, and there are few reports on the problem of multi-task service composition considering resource competition. However, in the real cloud manufacturing environment, multi-task service composition is common, so how to formulate a robust multi-task service composition model is a meaningful research direction. This paper first proposes a multi-task service composition optimization model considering resource competition constraints (MTSCOM-RCC) in the cloud manufacturing environment to fill the above-mentioned gap. The MTSCOM-RCC model considers the conflicts of interest and time between parallel subtasks and proposes an important service evaluation indicator. Secondly, a two-layer coding method, including task layer and subtask, is proposed based on studying the MTSCOM-RCC’s competition mechanism and characteristics. An improved hybrid genetic artificial bee colony algorithm model is proposed to solve the multi-task model. The model is combined with the global optimization method and task queue optimization method. Experimental results prove that the two-layer task optimization method model is more feasible and effective than the other two solution models.
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