物流配送干扰管理问题的知识表示与建模方法
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摘要
物流配送系统具有高度的复杂性和不确定性,经常受到如客户需求量变动,客户时间窗变动,车辆故障等事件的干扰,这些事件被称为干扰事件。干扰事件有可能使正在执行的配送计划变得不可行,使系统变得不正常。干扰事件发生后,如何快速实时地生成应对干扰事件的调整方案,使系统受到的扰动最小,这是干扰管理(Disruption Management)致力解决的问题。因此,干扰事件处理的实时性和科学性是干扰管理的关键和核心问题。对干扰事件进行定性与定量分析,利用模型算法求解出干扰问题的应对方案,这是提高决策科学性的重要手段；利用计算机在线实时、快速高效地建模,是提高决策实时性的重要方法。然而,由于物流配送系统的高度复杂性以及干扰管理问题的动态实时性特征,现有的理论方法难以解决该类问题的实时建模问题。本文以提高物流配送干扰管理决策过程的实时性和科学性为目标,研究物流配送干扰管理问题的知识表示与基于知识的建模方法,由计算机在线实时实现模型自动构建和求解,得到科学有效的决策方案,以辅助支持物流配送干扰管理决策过程。本文的主要研究工作如下：
(1)物流配送干扰管理问题知识表示方法研究。剖析物流配送干扰问题及干扰管理决策过程的特点,分析配送系统的构成对象及其状态、干扰事件处理策略的要素；分别从干扰事件的表示、系统配送状态的表示以及扰动分析与判定规则的表示三个方面来研究物流配送干扰管理问题知识表示的三要素,提出基于三元组ESR的物流配送干扰管理问题的知识表示方法；实现了在线的扰动分析与判定过程,为后续模型的自动构建奠定基础。
(2)物流配送干扰管理问题基于知识的建模方法研究。剖析干扰处理策略、策略优化过程涉及到的模型与算法的特征；从策略的匹配、算法的选择与构造、模型的构建三个方面研究了建模知识的表示与建模流程的实现问题,提出了物流配送干扰管理问题基于知识的建模方法。
(3)物流配送干扰管理建模支持系统的实现。以上述的知识表示方法与基于知识的建模方法为理论基础,设计并实现物流配送干扰管理建模支持原型系统。分析系统的功能、结构等,在Microsoft Windows平台上,使用“Amzi!Prolog"、"Visual C++"、"Access"等开发工具,设计并实现配送状态实时监控子系统、人机交互子系统、知识库子系统、数据库子系统、模型库子系统以及实时建模与问题求解子系统六个子系统；用算例对基于知识的建模方法的效果进行验证；并结合中石油大连销售分公司市内配送业务开展了应用研究,初见成效。
本研究是人工智能、知识工程理论与运筹学优化理论的交叉与渗透,对物流配送干扰管理过程的在线实时决策这一难题进行了有益的探索。本项研究集成物流配送车辆的数据实时采集、监控等技术,可以为物流配送过程的实时调度工作提供决策支持,对物流配送企业提高服务质量具有重要的现实意义,对求解与物流配送干扰管理相似的多目标动态规划问题也具有重要的理论意义。
Distribution system is complicated and has uncertainties. The distribution process is frequently disrupted by the events of customers changing their demands, changing their delivery times, vehicle breakdowns, etc. These events are named unexpected events, which may disable the distribution plan being executed and make the distribution system abnormal. Disruption management is a method which should promptly produce a new distribution plan deviating least from the original one after an unexpected event emerges. Hence, the key problem of disruption management is how the unexpected events can be handled in real time and in a scientific way. Based on the qualitative and quantitative analysis of the unexpected events, utilizing models and algorithms to produce a solution is one of the important strategies for improving the science of decision making, while utilizing the computer to efficiently realize the process of online modeling in real time is one of the important methods for improving the instantaneity of the decision process. However, due to the complexity of the distribution system, and the real-time and dynamic characteristics of disruption management problems, it's hard for existing theoretical methods to solve the real-time modeling of this kind of problem. This research aims to improve the science of the decision making and the instantaneity of the decision process in disruption management of the urban distribution. It studies the knowledge representation method for disruption management problems in urban distribution decisions. And based on the representation method, it studies the real-time and online computer-aided modeling and problem-solving method that can achieve effective plans for supporting the decision-making process of disruption management in distribution decisions. This research includes the following aspects.
(1) The knowledge representation method for disruption management problems in distribution decisions is studied. The disruption management problem and the disruption management process are analyzed. The objects composing the distribution system are abstracted and their states are analyzed. The factors of policies used to handle unexpected events are analyzed. The elements of knowledge representation for disruption management problems are studied from three aspects, the representation of unexpected events, the representation of distribution states, and the representation of rules for analyzing and identifying disruptions. Based on the above research, the three-tuple-based knowledge representation method, ESR, for disruption management problems in urban distribution decisions is proposed. The online process of analyzing and idenfying disruptions is realized, which sets the foundation for the subsequent modeling process.
(2) The knowledge-based modeling method for disruption management problems in distribution decisions is studied. The characteristics of disruption-handling policies, algorithms and models involved in the policy-optimizing processes are analyzed. The representation methods for the knowledge of modeling and the realization of the process of modeling are studied from the three aspects, the matching of the policies, the selecting and constructing of algorithms, and the constructing of models. Based on the above research, the knowledge-based modeling method for disruption management problems in distribution decisions is proposed.
(3) The modeling support system for the decision making of disruption management is developed. Based on the above mentioned theoretical research of the knowledge representation method and the knowledge-based modeling method, a prototype of modeling support system for disruption management in distribution decisions is designed and developed. Based on the analysis of the functions and the structure of the system, six subsystems are designed and developed by using the tools of "Amzi!Prolog", "Visual C++", "Access", etc. The six subsystems are the subsystem of real-time monitoring the distribution state, the subsystem of man-machine interaction, the subsystem of knowledge base, the subsystem of database, the subsystem of model base, and the subsystem of real-time modeling and problem solving. Moreover, a computational experiment is conducted to prove the efficiency and effectiveness of the proposed modeling method. Finally, the prototype is tested by real-world cases from Dalian Marketing Subsidiary of PetroChina Corporation.
The research is an intersection of the theories of Artificial Intelligence, Knowledge Engineering and Operations Research Optimization, which is a beneficial exploration for implementing the disruption management process in real time and on line. The research results combining with the techniques of collecting vehicle's data and monitoring vehicles in real time can provide decision support for real-time scheduling of the distribution process. The research is significant in the sense of improving the service quality of distribution companies. The methodology presented in the research provides a theoretical reference for solving multi-goal programming problems similar to disruption management problems in urban distribution decisions.
(1)物流配送干扰管理问题知识表示方法研究。剖析物流配送干扰问题及干扰管理决策过程的特点,分析配送系统的构成对象及其状态、干扰事件处理策略的要素；分别从干扰事件的表示、系统配送状态的表示以及扰动分析与判定规则的表示三个方面来研究物流配送干扰管理问题知识表示的三要素,提出基于三元组ESR的物流配送干扰管理问题的知识表示方法；实现了在线的扰动分析与判定过程,为后续模型的自动构建奠定基础。
(2)物流配送干扰管理问题基于知识的建模方法研究。剖析干扰处理策略、策略优化过程涉及到的模型与算法的特征；从策略的匹配、算法的选择与构造、模型的构建三个方面研究了建模知识的表示与建模流程的实现问题,提出了物流配送干扰管理问题基于知识的建模方法。
(3)物流配送干扰管理建模支持系统的实现。以上述的知识表示方法与基于知识的建模方法为理论基础,设计并实现物流配送干扰管理建模支持原型系统。分析系统的功能、结构等,在Microsoft Windows平台上,使用“Amzi!Prolog"、"Visual C++"、"Access"等开发工具,设计并实现配送状态实时监控子系统、人机交互子系统、知识库子系统、数据库子系统、模型库子系统以及实时建模与问题求解子系统六个子系统；用算例对基于知识的建模方法的效果进行验证；并结合中石油大连销售分公司市内配送业务开展了应用研究,初见成效。
本研究是人工智能、知识工程理论与运筹学优化理论的交叉与渗透,对物流配送干扰管理过程的在线实时决策这一难题进行了有益的探索。本项研究集成物流配送车辆的数据实时采集、监控等技术,可以为物流配送过程的实时调度工作提供决策支持,对物流配送企业提高服务质量具有重要的现实意义,对求解与物流配送干扰管理相似的多目标动态规划问题也具有重要的理论意义。
Distribution system is complicated and has uncertainties. The distribution process is frequently disrupted by the events of customers changing their demands, changing their delivery times, vehicle breakdowns, etc. These events are named unexpected events, which may disable the distribution plan being executed and make the distribution system abnormal. Disruption management is a method which should promptly produce a new distribution plan deviating least from the original one after an unexpected event emerges. Hence, the key problem of disruption management is how the unexpected events can be handled in real time and in a scientific way. Based on the qualitative and quantitative analysis of the unexpected events, utilizing models and algorithms to produce a solution is one of the important strategies for improving the science of decision making, while utilizing the computer to efficiently realize the process of online modeling in real time is one of the important methods for improving the instantaneity of the decision process. However, due to the complexity of the distribution system, and the real-time and dynamic characteristics of disruption management problems, it's hard for existing theoretical methods to solve the real-time modeling of this kind of problem. This research aims to improve the science of the decision making and the instantaneity of the decision process in disruption management of the urban distribution. It studies the knowledge representation method for disruption management problems in urban distribution decisions. And based on the representation method, it studies the real-time and online computer-aided modeling and problem-solving method that can achieve effective plans for supporting the decision-making process of disruption management in distribution decisions. This research includes the following aspects.
(1) The knowledge representation method for disruption management problems in distribution decisions is studied. The disruption management problem and the disruption management process are analyzed. The objects composing the distribution system are abstracted and their states are analyzed. The factors of policies used to handle unexpected events are analyzed. The elements of knowledge representation for disruption management problems are studied from three aspects, the representation of unexpected events, the representation of distribution states, and the representation of rules for analyzing and identifying disruptions. Based on the above research, the three-tuple-based knowledge representation method, ESR, for disruption management problems in urban distribution decisions is proposed. The online process of analyzing and idenfying disruptions is realized, which sets the foundation for the subsequent modeling process.
(2) The knowledge-based modeling method for disruption management problems in distribution decisions is studied. The characteristics of disruption-handling policies, algorithms and models involved in the policy-optimizing processes are analyzed. The representation methods for the knowledge of modeling and the realization of the process of modeling are studied from the three aspects, the matching of the policies, the selecting and constructing of algorithms, and the constructing of models. Based on the above research, the knowledge-based modeling method for disruption management problems in distribution decisions is proposed.
(3) The modeling support system for the decision making of disruption management is developed. Based on the above mentioned theoretical research of the knowledge representation method and the knowledge-based modeling method, a prototype of modeling support system for disruption management in distribution decisions is designed and developed. Based on the analysis of the functions and the structure of the system, six subsystems are designed and developed by using the tools of "Amzi!Prolog", "Visual C++", "Access", etc. The six subsystems are the subsystem of real-time monitoring the distribution state, the subsystem of man-machine interaction, the subsystem of knowledge base, the subsystem of database, the subsystem of model base, and the subsystem of real-time modeling and problem solving. Moreover, a computational experiment is conducted to prove the efficiency and effectiveness of the proposed modeling method. Finally, the prototype is tested by real-world cases from Dalian Marketing Subsidiary of PetroChina Corporation.
The research is an intersection of the theories of Artificial Intelligence, Knowledge Engineering and Operations Research Optimization, which is a beneficial exploration for implementing the disruption management process in real time and on line. The research results combining with the techniques of collecting vehicle's data and monitoring vehicles in real time can provide decision support for real-time scheduling of the distribution process. The research is significant in the sense of improving the service quality of distribution companies. The methodology presented in the research provides a theoretical reference for solving multi-goal programming problems similar to disruption management problems in urban distribution decisions.
引文
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