面向不确定性的供应链性能优化技术研究
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摘要
近几年来,全球市场环境发生了重大的变化:市场全球化更加明显、客户需求更为苛求、新产品开发过程加快。供应链是企业面对竞争激烈的市场环境,为增加竞争优势普遍所采用的技术。美国前财长鲁宾说:“关于市场,唯一确定的就是不确定。”不确定性是供应链的重要特点,而它本身的特点和所处的环境也决定了不确定性是供应链优化中的难点。
本文结合国家863课题和浙江省科技攻关项目,对供应链不确定性的传播机理、供应链不确定性影响的控制策略、供应链性能、供应链静态性能优化、供应链不确定性的传播过程描述和影响模型、供应链动态性能优化、仿真优化技术等方面进行了深入研究。
第一章回顾了供应链的产生过程,阐明了面向不确定性的供应链优化的研究意义。从面向不确定性的供应链优化技术和不确定性对供应链的影响两个方面出发,概括了国内外研究现状,指出了其中存在的不足。在此基础上,给出了本文的研究目标、研究内容和论文结构。
第二章对供应链中的不确定性现象进行了分类,给出了本文研究的四种不确定性。分析了供应链不确定性的传播过程,研究了传播机理和控制策略。分析了面向不确定性的供应链优化途径、目标和技术,将供应链性能分为供应链静态性能和动态性能,提出了面向不确定性的供应链性能优化解决方案。
第三章以增强供应链抵抗不确定性的能力为目标,研究了供应链静态性能优化方法。该方法包括:供应链网络优化、静态性能多目标模糊优化和静态性能仿真。其中静态性能仿真检验了网络优化和静态性能多目标模糊优化的结果的合理性和有效性。
第四章研究了供应链不确定性的三个层次的模型化表述:不确定性的单重描述、传播过程描述和影响模型。针对建立传播过程描述和影响模型过程中出现的数据缺失、关系描述不清、数据不平衡、时间动态性等问题,给出了相应的特殊工具——数据快速修复、自发节点模型、网络数据融合、动态影响图。
第五章以对不确定性进行有效地动态控制为目的,探讨了供应链动态性能优化方法。该方法以动态影响图为基础理论工具,建立了相应的仿真模型,探讨了基于仿真优化技术的模型的求解方法。计算实例表明,仿真优化计算技术和动态影响图建模技术在供应链动态性能优化中的可行性和有效性。
第六章对全文进行了总结,对今后的研究工作进行了展望。
In recent years, the global market environment has been changed obviously: globalized market, exact customer demand, shorten new product development. Supply chain is the competitive advantage technology when enterprises face highly competitive market environment. Former U.S. Treasury Secretary Rubin said: "With regard to the market, the only determined thing is uncertainty." According to the inherent characteristics of supply chain, ignoring supply chain uncertainty is impossible and uncertainty is remarkable difficult for supply chain management and optimization.
With the support from the National High Technology Research and Development Program of China and Key Scientific & Technological Research Projects of Zhejiang Province, the spread mechanism, control strategy, spread process description, and influence model of supply chain uncertainty, supply chain performance, static performance optimization, dynamic performance optimization, and simulation optimization technology have been intensively studied.
In chapterⅠ, based on the review of supply chain management, we elaborated the significance of uncertainty-oriented supply chain optimization research. Then, according to the current research progress and the shortcomings of domestic and international supply chain uncertainty optimization, we proposed the research objective, main contents and framework of the dissertation.
In chapterⅡ, after classifying the typical uncertain phenomenon in supply chain, 4 kinds of supply chain uncertainty were presented. Then, through the analysis of the spread process, the spread mechanism and control strategy of supply chain uncertainty were studied. On the basis of analyzing the approach, objective and technology of uncertainty-oriented supply chain optimization, supply chain performance divided into static and dynamic performance and the uncertainty-oriented optimization solution of supply chain performance was proposed.
In chapterⅢ, to enhance the ability of resisting supply chain uncertainty, the method of supply chain static performance optimization was studied. The method includes 3 parts: supply chain network optimization, multi-objective fuzzy optimization, and performance simulation of supply chain static performance. Furthermore, the reasonableness and effectiveness of supply chain network optimization and multi-objective fuzzy optimization result were verified by performance simulation.
In chapterⅣ, 3 descriptive layers of supply chain uncertainty were proposed, i.e., single description, spread process description and influence model of supply chain uncertainty. In addition, the difficulties when establishing the spread process description and influence model, such as data loss, unclear relationship description, imbalance sample data and dynamic were dealt with, via the corresponding tools, i.e., rapid data recovery, Noisy-OR gates model, and network data amalgamation were advanced.
In chapterⅤ, to control supply chain uncertainty dynamically and effectively, the method of supply chain dynamic performance optimization was discussed. The method was based on Dynamic Influence Diagrams, established the corresponding simulation model and the solving method based on simulation optimization technology. In addition, by means of calculating the numerical example, the feasibility and effectiveness of simulation optimization and Dynamic Influence Diagrams in supply chain dynamic performance optimization were proved.
In chapterⅥ, a summary of the dissertation and the future work of this research were discussed in the final section of the dissertation.
本文结合国家863课题和浙江省科技攻关项目,对供应链不确定性的传播机理、供应链不确定性影响的控制策略、供应链性能、供应链静态性能优化、供应链不确定性的传播过程描述和影响模型、供应链动态性能优化、仿真优化技术等方面进行了深入研究。
第一章回顾了供应链的产生过程,阐明了面向不确定性的供应链优化的研究意义。从面向不确定性的供应链优化技术和不确定性对供应链的影响两个方面出发,概括了国内外研究现状,指出了其中存在的不足。在此基础上,给出了本文的研究目标、研究内容和论文结构。
第二章对供应链中的不确定性现象进行了分类,给出了本文研究的四种不确定性。分析了供应链不确定性的传播过程,研究了传播机理和控制策略。分析了面向不确定性的供应链优化途径、目标和技术,将供应链性能分为供应链静态性能和动态性能,提出了面向不确定性的供应链性能优化解决方案。
第三章以增强供应链抵抗不确定性的能力为目标,研究了供应链静态性能优化方法。该方法包括:供应链网络优化、静态性能多目标模糊优化和静态性能仿真。其中静态性能仿真检验了网络优化和静态性能多目标模糊优化的结果的合理性和有效性。
第四章研究了供应链不确定性的三个层次的模型化表述:不确定性的单重描述、传播过程描述和影响模型。针对建立传播过程描述和影响模型过程中出现的数据缺失、关系描述不清、数据不平衡、时间动态性等问题,给出了相应的特殊工具——数据快速修复、自发节点模型、网络数据融合、动态影响图。
第五章以对不确定性进行有效地动态控制为目的,探讨了供应链动态性能优化方法。该方法以动态影响图为基础理论工具,建立了相应的仿真模型,探讨了基于仿真优化技术的模型的求解方法。计算实例表明,仿真优化计算技术和动态影响图建模技术在供应链动态性能优化中的可行性和有效性。
第六章对全文进行了总结,对今后的研究工作进行了展望。
In recent years, the global market environment has been changed obviously: globalized market, exact customer demand, shorten new product development. Supply chain is the competitive advantage technology when enterprises face highly competitive market environment. Former U.S. Treasury Secretary Rubin said: "With regard to the market, the only determined thing is uncertainty." According to the inherent characteristics of supply chain, ignoring supply chain uncertainty is impossible and uncertainty is remarkable difficult for supply chain management and optimization.
With the support from the National High Technology Research and Development Program of China and Key Scientific & Technological Research Projects of Zhejiang Province, the spread mechanism, control strategy, spread process description, and influence model of supply chain uncertainty, supply chain performance, static performance optimization, dynamic performance optimization, and simulation optimization technology have been intensively studied.
In chapterⅠ, based on the review of supply chain management, we elaborated the significance of uncertainty-oriented supply chain optimization research. Then, according to the current research progress and the shortcomings of domestic and international supply chain uncertainty optimization, we proposed the research objective, main contents and framework of the dissertation.
In chapterⅡ, after classifying the typical uncertain phenomenon in supply chain, 4 kinds of supply chain uncertainty were presented. Then, through the analysis of the spread process, the spread mechanism and control strategy of supply chain uncertainty were studied. On the basis of analyzing the approach, objective and technology of uncertainty-oriented supply chain optimization, supply chain performance divided into static and dynamic performance and the uncertainty-oriented optimization solution of supply chain performance was proposed.
In chapterⅢ, to enhance the ability of resisting supply chain uncertainty, the method of supply chain static performance optimization was studied. The method includes 3 parts: supply chain network optimization, multi-objective fuzzy optimization, and performance simulation of supply chain static performance. Furthermore, the reasonableness and effectiveness of supply chain network optimization and multi-objective fuzzy optimization result were verified by performance simulation.
In chapterⅣ, 3 descriptive layers of supply chain uncertainty were proposed, i.e., single description, spread process description and influence model of supply chain uncertainty. In addition, the difficulties when establishing the spread process description and influence model, such as data loss, unclear relationship description, imbalance sample data and dynamic were dealt with, via the corresponding tools, i.e., rapid data recovery, Noisy-OR gates model, and network data amalgamation were advanced.
In chapterⅤ, to control supply chain uncertainty dynamically and effectively, the method of supply chain dynamic performance optimization was discussed. The method was based on Dynamic Influence Diagrams, established the corresponding simulation model and the solving method based on simulation optimization technology. In addition, by means of calculating the numerical example, the feasibility and effectiveness of simulation optimization and Dynamic Influence Diagrams in supply chain dynamic performance optimization were proved.
In chapterⅥ, a summary of the dissertation and the future work of this research were discussed in the final section of the dissertation.
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