Modeling and simulation of task rescheduling strategy with resource substitution in cloud manufacturing

Author:

Zhang Xiaodong,Ren Dawei

Abstract

<abstract> <p>When a cloud manufacturing environment extends to multi-user agent, multi-service agent and multi-regional spaces, the process of manufacturing services faces increased disturbances. When a task exception occurs because of disturbance, it is necessary to quickly reschedule the service task. We propose a multi-agent simulation modeling approach to simulate and evaluate the service process and task rescheduling strategy of cloud manufacturing, with which impact parameters can be achieved through careful study under different system disturbances. First, the simulation evaluation index is designed. In addition to the quality of service index of cloud manufacturing, the adaptive ability of task rescheduling strategy in response to a system disturbance is considered, and the flexibility of cloud manufacturing service index is proposed. Second, considering the substitution of resources, the internal and external transfer strategies of service providers are proposed. Finally, a simulation model of the cloud manufacturing service process of a complex electronic product is constructed by multi-agent simulation, and simulation experiments under multiple dynamic environments are designed to evaluate different task rescheduling strategies. The experimental results indicate that the external transfer strategy of the service provider in this case has higher quality of service and flexibility of service. Sensitivity analysis indicates that the matching rate of substitute resources for internal transfer strategy of service providers and the logistics distance of external transfer strategy of service providers are both sensitive parameters, which have significant impacts on the evaluation indexes.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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