【作者】 王龙；

【导师】 陈友玲；

【作者基本信息】 重庆大学 ， 机械工程（工业工程）， 2019， 硕士

【摘要】 随着新兴信息技术的快速发展,传统制造企业加速转型升级,云制造应运而生。云制造通过整合多个不同领域的、不同地域的制造资源,实现分布式制造资源的集中管理及优化配置,逐渐突破制造行业资源的共享程度和使用效率的限制,从而促使资源的全面共享,提高资源的利用率。此外,由于制造业的分工越来越趋于细化和专业化,企业用户更注重于发展自身核心竞争力,导致云制造环境下制造任务往往是通过资源的服务组合来完成。然而云平台拥有大量功能相同或相近但服务质量不同的资源服务,如何快速地从海量组合方案中挑选出满足多方利益需求的最佳服务组合并成功执行任务,已成为当前云制造服务组合与优选的关键问题。因此,本文提出了一种云制造环境下制造资源服务组合与优选的新方法。首先,论文研究了云制造任务分解,考虑资源池状态对任务分解的影响,改进了云制造任务分解方法,将任务分解环节纳入服务组合过程,避免任务分解与资源配置脱节,实现任务与服务资源的精确匹配。其次,研究了资源服务组合模型及其求解方法,综合考虑任务方、服务方和平台方的三方利益需求,提出了任务质量约束信息控制方法,引入任务质量控制后的置信区间,进行了服务组合冲突预防并建立了服务组合数学模型;提出了改进的带有精英策略的快速非支配排序遗传算法,求解模型得到服务组合的最优解集,并通过测试,验证了算法的有效性和高效性。然后,研究了服务组合优选评估与服务组合冲突解决,提出了改进的灰色关联度分析方法和服务组合冲突解决方法。一是对服务组合的最优解集进行评估优选,为决策者提供最优服务组合和备选方案集;二是分析了不同服务组合冲突,并提出相应冲突解决方案,从实际角度为用户动态推送优选的服务组合,实现了服务组合的动态优选。最后,对论文的研究成果进行了案例应用,并阐述研究过程。通过实例证明了本文所提出的方法的合理性和有效性。更多还原

【Abstract】 With the rapid development and wide application of modern information technologies,the traditional manufacturing enterprises have undergone profound and constant changes constantly.Under this background,cloud manufacturing was put out in recent years.Cloud manufacturing has gradually broken through the limitation of the sharing degree and utilization of resources in the manufacturing industry by integrating distributed manufacturing resources,realizing the idea of centralized management and optimal configuration,thereby promotes the comprehensive sharing of resources and improves utilization of resource.Moreover,enterprise users pay more attention to develop their core competitiveness due to the division of labor becomes more and more refined and specialized in the manufacturing industry,which makes the manufacturing tasks are often completed through a composition of resource services in the cloud manufacturing environment.However,there are a large number of cloud services share similar or even overlapped functionalities but different service qualities in the cloud platform,and how to quickly select the best service composition to meet the interest needs of the three parties from the mass composition solutions and successfully execute the task,has become a key issue for manufacturing resource service composition and optimization.Based on the above analysis,this paper proposes a new method of manufacturing resource service composition and optimization in current cloud manufacturing environment.First of all,the paper studies the manufacturing task decomposition.Considering the impact of the resource pool condition during the task decomposition process,an improved manufacturing task decomposition method is proposed.And then,incorporating the task decomposition into the process of service composition,which aims to avoid the disconnection between task decomposition and resource allocation.Secondly,the paper studies the service composition model and its solution method.And it comprehensively considers the interest needs of the three parties,proposes the control method of the constraint information about quality of task,introduces the confidence interval on the top of task quality control,conducts the service composition conflict prevention,and establishes mathematical model.Besides,an improved fast non-dominated sorting genetic algorithm with elite strategy is proposed.Moreover the Pareto optimal solution set of the service composition is obtained by solving the model,and the effectiveness and efficiency of the algorithm are verified.Thirdly,the paper studies the service composition optimization and the service composition conflict resolution,and proposes an impoved grey relation analysis method and a service composition conflict resolution method.For one thing,the Pareto optimal solution set of the service composition is evaluated and optimized,which provides the optimal service composition and alternative solution set for the decision makers.For another,this paper analyzes the different kinds of service composition conflicts,proposes corresponding conflict resolutions to dynamically push the preferred service composition for the user from a practical perspective,and achieves dynamic optimization of service composition.Finally,the study results of this paper are applied in practice,and the process is illustrated.Then the rationality and effectiveness of the proposed method are proved by the application results.更多还原