网购物流共同配送环境下任务形式化表征与分解方法研究

发布时间：2018-01-08 12:20

本文关键词：网购物流共同配送环境下任务形式化表征与分解方法研究　出处：《重庆大学》2015年硕士论文　论文类型：学位论文

【摘要】：网络购物的发展给网商、消费者带来实惠的同时,也对物流配送的服务能力提出了更高的要求,共同配送是企业间进行联合配送的模式,能够有效整合各企业物流资源,扩大市场辐射范围,一定程度上提升物流能力,降低物流成本,提高资源利用率,促进企业与社会的良性发展。然而,该模式并未从根本上解决物流成本偏高、响应速度慢等问题,一方面,目前对物流任务的描述还比较粗浅,凭借经验的物流任务分解方法存在不合理性,导致分解得到的子任务难以匹配合适的物流资源以及在联盟企业间进行合理分配;另一方面,当前缺乏有效的表征方法来将物流任务形式化为计算机可以识别、理解和执行的格式,也缺乏有效的任务分解方法来支撑在计算机环境下实现物流任务的自动化分解,从而导致了共同配送模式下各企业间无法高效的进行物流任务的合理分配与协同运作。因此,如何提高物流任务分解的效率并保证任务分解结果的合理性,是网购物流共同配送环境下面临的一个关键问题。根据网购环境下物流任务的特征分析,本文利用本体表示方法实现物流任务的形式化表征,并采用基于层级任务网络(HTN)规划的任务分解方法支持计算机环境下物流任务合理、高效的分解。首先,通过分析网购以及网购环境下物流任务的特点,明确物流任务的非功能需求、功能需求以及任务基本信息等属性描述,梳理清楚父、子任务之间的组合关系以及子任务之间的时序、逻辑关系;在此基础上,利用本体表示方法,将物流任务以及任务分解过程中的约束条件形式化成计算机可以识别、理解和执行的格式,为实现计算机环境下的物流任务分解提供支撑。然后,选用HTN规划方法来支持计算机环境下的物流任务分解,对HTN规划方法进行面向物流领域的扩展,使该方法能够较好的适用于网购物流环境下的任务分解问题;基于面向物流领域的扩展,提出基于扩展HTN规划的物流任务分解过程,并设计相应算法以支持物流任务分解过程的实现。最后,基于本文提出的扩展HTN规划任务分解方法,针对一个物流配送任务需求实例进行求解,验证了方法的可行性,并对改进前后的HTN规划任务分解算法进行实验对比,说明改进后的HTN规划任务分解方法更加有效和稳定。
[Abstract]:The development of online shopping brings benefits to consumers, but also puts forward higher requirements for the service ability of logistics distribution. Joint distribution is the mode of joint distribution among enterprises. Can effectively integrate the logistics resources of enterprises, expand the scope of market radiation, to a certain extent improve logistics capacity, reduce logistics costs, improve the utilization of resources, and promote the healthy development of enterprises and society. This model has not fundamentally solved the problems of high logistics cost and slow response. On the one hand, the description of logistics task is relatively shallow, and the decomposition method of logistics task based on experience is unreasonable. As a result, the decomposed sub-tasks are difficult to match the appropriate logistics resources and distribute reasonably among the alliance enterprises. On the other hand, there is a lack of effective representation to formalize logistics tasks into a format that can be recognized, understood and executed by computers. There is also a lack of effective task decomposition methods to support the automated decomposition of logistics tasks in the computer environment. Therefore, how to improve the efficiency of logistics task decomposition and ensure the rationality of task decomposition results. According to the characteristic analysis of logistics task in online shopping environment, this paper uses ontology representation method to realize the formal representation of logistics task. The task decomposition method based on hierarchical task network (HTN) planning is used to support the reasonable and efficient decomposition of logistics tasks in computer environment. Firstly, the characteristics of logistics tasks in online shopping and online shopping environment are analyzed. Define the non-functional requirements, functional requirements and basic information of logistics tasks, and sort out the combination of parent and sub-tasks, as well as the temporal and logical relationships between sub-tasks; On this basis, using ontology representation method, the form of constraints in the process of logistics task and task decomposition is transformed into a format that can be recognized, understood and executed by computer. Then, HTN programming method is selected to support the logistics task decomposition in computer environment, and the HTN planning method is extended to the logistics field. This method can be applied to the task decomposition problem in the online shopping logistics environment. Based on the extension of logistics domain, this paper proposes a logistics task decomposition process based on extended HTN programming, and designs the corresponding algorithm to support the realization of logistics task decomposition process. Based on the extended HTN planning task decomposition method proposed in this paper, the feasibility of the method is verified by solving an example of logistics distribution task requirement. The experimental results show that the improved HTN planning task decomposition method is more effective and stable.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F724.6;F259.2

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