| 领域QoS与资源感知的物流服务动态优化组合方法 |
| |
| 作者姓名： | 徐园园 刘志中 贾宗璞 |
| |
| 作者单位： | 河南理工大学计算机科学与技术学院,焦作,454000;河南理工大学计算机科学与技术学院,焦作,454000;河南理工大学计算机科学与技术学院,焦作,454000 |
| |
| 摘 要： | 为了提高物流服务优化组合的动态性、可靠性与用户满意度,本文提出了一种基于全局服务质量(quality of service,QoS)约束分解的能够感知领域质量与资源需求的物流服务优化组合方法.该研究工作首先把学习机制引入人工蜂群算法(artificial bee colony algorithm,ABC),形成了具有自主学习能力的改进型人工蜂群算法(LABC);之后,应用学习人工蜂群算法(LABC)将全局QoS约束分解成每个物流子任务需要满足的局部QoS约束,从而将QoS感知的物流服务优化组合这一全局优化问题转化成以领域质量为依据的局部最优服务选择问题;其次,在物流服务流程执行的过程中,在感知物流任务节点对资源需求的前提下,为每一个物流任务节点选择一个具有最优领域QoS的物流服务;与已有的研究工作相比,该方法能够实现物流服务动态可靠的优化组合.最后,通过模拟实验验证了本文所提出的方法是可行有效的.
|
| 关 键 词： | 领域QoS 资源感知 物流服务组合 QoS约束分解 人工蜂群算法 |
| 收稿时间： | 2017-10-09 |
Domain QoS and resource-aware logistics web service dynamic optimal composition |
| |
| Affiliation: | College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China |
| |
| Abstract: | With the rapid development of service computing, cloud computing, internet of things, e-commerce, and modern logistics industry, cross-domain logistics services cooperation has become the main development trend of the modern logistics industry. The dynamic optimal composition of web services in logistics has become the key technology to create large and powerful logistics services based on the available logistics services of different companies that achieve seamless convergence of logistics services, satisfy user complex requirements, and realize the value addition. Recently, owing to the technologies of web services, cloud computing, and service sciences, an increasing number of logistics companies have registered themselves as logistics web service providers. The logistics services composition should satisfy the user's global QoS constraints and provide the best quality of service (QoS.) Currently, with the rapid development of cloud computing, e-commerce, service computing, and modern logistics industry, many logistics services are available on the network providing similar functions and different levels of QoS. These factors make the problem of determining the optimal composition of a logistics service a typical Np-hard problem. This study proposes a method to achieve the dynamic optimal composition of domain QoS and resource-aware logistics services and to realize logistics services that are dynamic, offer quality of domain services, and are aware of resource requirements. First, the learning artificial bee colony algorithm (LABC) is proposed; LABC is applied to decompose the global QoS constraints into local QoS constraints that logistics task nodes must satisfy and to transform the global optimization problem of logistics service composition into a local optimal service selection problem. Second, during the process of logistics service process execution, for each task node, the logistics service with best domain QoS evaluation, which can satisfy the local QoS constraints and resource requirements, is chosen to achieve a high-quality dynamic logistics service and optimal composition of service. The results of simulation experiments show that the proposed method is feasible and effective. |
| |
| Keywords: | |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《》浏览原始摘要信息 |
|
点击此处可从《》下载全文 |
|
