基于数据挖掘的物流运输系统研究
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摘要
随着物流业的迅猛发展,物流的数据信息也呈几何级增长,但目前的信息系统,通常无法对这些庞大的数据信息进行深入地分析,从而无法精确了解行业发展的规律、应当引导的方向。因此建立一种新型的信息系统十分必要。
传统的数据库管理信息系统不能够很好地利用、分析数据库中积累的大量数据,数据挖掘与数据仓库技术可以很好地解决这一问题。数据挖掘(Data Mining)是致力于数据分析和理解,揭示数据内部蕴藏知识的技术,它是未来信息技术应用的重要目标之一。它可以通过对大量的数据进行探索分析,挖掘出有意义的规则,以期待对未来的决策提供适当参考建议。近十几年来,数据挖掘技术已得到了广泛的研究及应用,并且在商业、金融、医疗等领域得到成功的应用。
本文研究物流运输系统的目的正是为了帮助物流配送企业随着市场需求变化,不断调整企业的运作方针,优化企业的业务流程,提高企业经营管理水平和企业竞争力。笔者将数据仓库技术、数据挖掘技术与物流配送调运模型结合建立的物流运输系统,能够帮助企业优化资源,为企业的发展做出辅助决策。
本文首先对物流进行了定义,阐述了当前物流配送在国内外的发展状况,重点介绍了我国物流运输成本居高不下的原因,指出传统物流管理系统运输模块的不足之处,提出将数据挖掘技术运用于物流管理系统的运输子系统中,用遗传算法解决配送线路优化问题。
阐述了数据挖掘的理论与技术方法,比较了数据挖掘与传统分析方法的区别和联系,指出数据挖掘在解决具有海量信息处理、发现数据特征、辅助决策等问题方面有较强的优势,满足物流管理系统对配送模块的需求。
详细介绍了物流配送路径优化问题的遗传算法的原理和动态规划法在物流管理系统运输决策模块中具体算法,得出了最优运输方式。
最后本文在对系统进行了全面的需求分析的基础上,根据实际需要用C++和Access设计了数据库结构和相关的应用程序,设计了物流运输模块的基本构架及部分程序,并作了部分仿真实验,实际应用效果表明,本文所提出的基于数据挖掘的物流运输系统,能提高运输效率,减少操作人员的工作量,优化配送路线,为决策者更快、更好地做出判断提供了重要的数据依据,预测结果较好地符合了实际情况,具备了进一步研究的学术价值和实际应用价值。
The article explains the problems of the current logistic info systems which include the shortage in quick response, decision support, united plan and customer relationship. Such system may cause losses to companies whose operations relies on them in a long run because none of them can provide a deeper insights and views of the future business, therefore adopting Data Mining technology is a good solution to tackle with such problems.
The traditional database Management Information System can not be made full use of, the analytical database is hit by build-up large amount of data , the data excavates with data storehouse technology to be able to resolve this one problem very good. That the data is excavated (Data Mining) is to concentrate efforts on data analysis and understand that , announce to the technology containing knowledge in the inside of data, it is one of the important target that future IT applies. It can probe analysis by being in progress to large amount of data , excavate out meaningful regulation , suggest that to look forward to providing the appropriate reference to future decision-making. The data excavates a technology coming being close to 10, already having got broad application and studying , is already applied successfully and in fields such as trade financing, medical treatment.
The systematic purpose of studying logistics transports is exactly being a distribution for helping logistics changes the main body of a book with the market demand , continual readjustment enterprise operation guiding principle, optimizes the enterprise business technological process , improves enterprise managerial and administrative expertise sum enterprise competition. The model the technology and logistics distribution data storehouse Tech Data, is excavated are allocated and transported transports system combining with the logistics building , is able to help enterprise to optimize resource , is that development of enterprise does out auxiliary decision of strategic importance.
This article first carries on the definition to logistics and elaborating the present situation of logistics allocation both in the domestic and foreign development.It emphasizes the cause why the logistics transportation cost stays at a high level in our country and points out the deficiency of the traditional module of logistics transportation system. It proposes utilizing the data mining technology into the transportation subsystem of the logistics management system and uses the genetic algorithm to solve the optimization problem of allocation line.
This article elaborates the data mining theory and the technical method.Comparing data excavation and the tradition analysis method,it points out the strong superiority of the data mining in processing magnanimous information, discovering data characteristic,and assisting decision,which can meet the needs of logistics management system to allocation module.
This article introduces how the hereditary principle and dynamic planning principle of logistics allocation optimization plays the concrete algorithm among the transportation decision module of logistics administrative system, and obtains the optimum transportation way.
This article analyzes the demand of the system comprehensively. According to the needs of reality, it designs database structure and relevant application program with C++ and Access and the basic framework of and some procedures the logistics transportion module. After some artificial experiments, it indicates that the logistics transportation system based on that the data mining can improve the transportation efficiency,reduce the attenbant's work load,and optimize the route of providing and delivering,which offers the important data basis for policymaker's quickly, better judging. The result of prediction has accorded with the actual conditions well, which possesses academic value and actual applicational value for further study.
传统的数据库管理信息系统不能够很好地利用、分析数据库中积累的大量数据,数据挖掘与数据仓库技术可以很好地解决这一问题。数据挖掘(Data Mining)是致力于数据分析和理解,揭示数据内部蕴藏知识的技术,它是未来信息技术应用的重要目标之一。它可以通过对大量的数据进行探索分析,挖掘出有意义的规则,以期待对未来的决策提供适当参考建议。近十几年来,数据挖掘技术已得到了广泛的研究及应用,并且在商业、金融、医疗等领域得到成功的应用。
本文研究物流运输系统的目的正是为了帮助物流配送企业随着市场需求变化,不断调整企业的运作方针,优化企业的业务流程,提高企业经营管理水平和企业竞争力。笔者将数据仓库技术、数据挖掘技术与物流配送调运模型结合建立的物流运输系统,能够帮助企业优化资源,为企业的发展做出辅助决策。
本文首先对物流进行了定义,阐述了当前物流配送在国内外的发展状况,重点介绍了我国物流运输成本居高不下的原因,指出传统物流管理系统运输模块的不足之处,提出将数据挖掘技术运用于物流管理系统的运输子系统中,用遗传算法解决配送线路优化问题。
阐述了数据挖掘的理论与技术方法,比较了数据挖掘与传统分析方法的区别和联系,指出数据挖掘在解决具有海量信息处理、发现数据特征、辅助决策等问题方面有较强的优势,满足物流管理系统对配送模块的需求。
详细介绍了物流配送路径优化问题的遗传算法的原理和动态规划法在物流管理系统运输决策模块中具体算法,得出了最优运输方式。
最后本文在对系统进行了全面的需求分析的基础上,根据实际需要用C++和Access设计了数据库结构和相关的应用程序,设计了物流运输模块的基本构架及部分程序,并作了部分仿真实验,实际应用效果表明,本文所提出的基于数据挖掘的物流运输系统,能提高运输效率,减少操作人员的工作量,优化配送路线,为决策者更快、更好地做出判断提供了重要的数据依据,预测结果较好地符合了实际情况,具备了进一步研究的学术价值和实际应用价值。
The article explains the problems of the current logistic info systems which include the shortage in quick response, decision support, united plan and customer relationship. Such system may cause losses to companies whose operations relies on them in a long run because none of them can provide a deeper insights and views of the future business, therefore adopting Data Mining technology is a good solution to tackle with such problems.
The traditional database Management Information System can not be made full use of, the analytical database is hit by build-up large amount of data , the data excavates with data storehouse technology to be able to resolve this one problem very good. That the data is excavated (Data Mining) is to concentrate efforts on data analysis and understand that , announce to the technology containing knowledge in the inside of data, it is one of the important target that future IT applies. It can probe analysis by being in progress to large amount of data , excavate out meaningful regulation , suggest that to look forward to providing the appropriate reference to future decision-making. The data excavates a technology coming being close to 10, already having got broad application and studying , is already applied successfully and in fields such as trade financing, medical treatment.
The systematic purpose of studying logistics transports is exactly being a distribution for helping logistics changes the main body of a book with the market demand , continual readjustment enterprise operation guiding principle, optimizes the enterprise business technological process , improves enterprise managerial and administrative expertise sum enterprise competition. The model the technology and logistics distribution data storehouse Tech Data, is excavated are allocated and transported transports system combining with the logistics building , is able to help enterprise to optimize resource , is that development of enterprise does out auxiliary decision of strategic importance.
This article first carries on the definition to logistics and elaborating the present situation of logistics allocation both in the domestic and foreign development.It emphasizes the cause why the logistics transportation cost stays at a high level in our country and points out the deficiency of the traditional module of logistics transportation system. It proposes utilizing the data mining technology into the transportation subsystem of the logistics management system and uses the genetic algorithm to solve the optimization problem of allocation line.
This article elaborates the data mining theory and the technical method.Comparing data excavation and the tradition analysis method,it points out the strong superiority of the data mining in processing magnanimous information, discovering data characteristic,and assisting decision,which can meet the needs of logistics management system to allocation module.
This article introduces how the hereditary principle and dynamic planning principle of logistics allocation optimization plays the concrete algorithm among the transportation decision module of logistics administrative system, and obtains the optimum transportation way.
This article analyzes the demand of the system comprehensively. According to the needs of reality, it designs database structure and relevant application program with C++ and Access and the basic framework of and some procedures the logistics transportion module. After some artificial experiments, it indicates that the logistics transportation system based on that the data mining can improve the transportation efficiency,reduce the attenbant's work load,and optimize the route of providing and delivering,which offers the important data basis for policymaker's quickly, better judging. The result of prediction has accorded with the actual conditions well, which possesses academic value and actual applicational value for further study.
引文
[1]康晓东.基于数据仓库的数据挖掘技术[M].北京:机械工业出版社,2004:13-15.
[2] (美)Steve Hoberman著,贾爱霞,程耀译.数据建模[M].北京:机械工业出版社,2004:28-36
[3] Holland著.自然和人工系统中的适应性[M].1975.
[4]李敏强.遗传算法的基本理论与应用[M].北京:科学出版社,2003.
[5]郎茂祥,基于遗传算法的物流配送路径优化问题研究[J],中国公路学报.系统工程理论与实践,1999,22-25.
[6] Z.米凯利维茨,演化程序-遗传算法和数据编码的结合[M],北京:科学出版社,2000.
[7] Climaco J C,Alves M.Interactive Decision Support for Multiobjective Transportation Problem.European Journal of Operational Research[M],1993(65):58-67.
[8] Vignaux G A,Michalewicz Z.A Genetic Algorithm for the Linear Transportation Problem.IEEE Transportations on Systems,Man,and Cybernetics[M],1991(21):445-452.
[9] Michalewicz Z,Vignaux G A,Hobbs M.A Non-Standard Genetic Algorithm for the Nonlinear Transportation Problems.ORSA Journal on Computing[M], 1991,3(4):307-316.
[10]王日爽,徐兵等.应用动态规划.国防工业大学出版社[M],1987.7,第一版.
[11]陶谦坎.运筹学.西安交通大学出版社[M],1987.7,第一版.
[12]金升平.城市车辆配送线路设计的模型与算法[J].计算机工程与应用,2002,22 .
[13]谢如鹤,罗荣武,张得志等编著,物流系统规划原理与方法[M]中国物资出版社,2004.3.
[14]徐利群.物流运输路线选择与成本节约[J].上海管理科学,2004,4,49-50.
[15]林杰等,数据挖掘与OLAP理论与实务[M],北京:清华大学出版社,2003,第一版.
[16]郝渊晓,现代物流配送管理[M],中山大学出版社,2001.11.
[17]蒲春梅,王延清.物流运输中的隐性成本及其控制[J].商业时代,2005,15.
[18]李岩,申金升,沈昆.城市物流运输的可持续发展建议[J].山西交通科技杂志,2004,2.
[19]陈国良,王煦法,庄镇泉等,遗传算法及其应用[M],北京:人民邮电出版社,1996 .
[20] Mickey Yost.Data warehousing and decisinon support at the National Agricultural Statistics Service.Social Science Computer Review[M],2000.18(4):434-441.
[21]余达征,索丽生,史金松.基于数据仓库的数据挖掘技术及其在防洪调控智能决策支持系统(FCDIDSS)中的应用[J].水文,2001,21(2):5-8.
[22] Z.米凯利维茨,演化程序-遗传算法和数据编码的结合[M],北京:科学出版社,2000.
[23]王之泰.现代物流学[M].北京:中国物资出版社,2001. 45-70.
[24]王日爽,徐兵等.应用动态规划[M].国防工业大学出版社,1987.7,第一版.
[25]陶谦坎.运筹学[M].西安交通大学出版社,1987.7,第一版.
[26] W.H.Inmon.Building thd Data Warehouse[M].QED Technical publishing Group,1992 .
[27]邵举平.物流信息系统[M].北京:清华大学出版社,2005:56一78.
[28] R.Brachman and T.Anand.The Process of Knowledge Discovery in Databases:A Human Centered Approach.Advances in Knowledge Discovery and Data Mining,AAAI/MIT Press[M],1996.
[29]启明工作室.Visual C++ +SQL Server数据库应用系统开发与实例[M].北京人民邮电出版社,2004.07.
[30]黄维通.Visual C++面向对象与可视化程序设计习题解析与编程实例[M].北京:清华大学出版社,2003.12.
[31]刘耀宗.基于数据仓库的物流信息系统[D].南京:南京理工大学软件工程系,2004.
[32]周明,孙树栋.遗传算法原理及其应用[M].北京:国防工业出版社,1999.
[33]李敏强,寇纪松,林月,李书全.遗传算法的基本理论与应用[M].北京:科学出版社,2002.
[34]乐阳,龚健雅,Dijkstra最短路径算法的一种高效实现[J],武汉测绘科技大学学报,1999,Vol.24 No.3:209-212..
[35]辛香非,朱鑫.遗传算法的适应度函数研究[J].系统工程与电子技术,1998,(11):58-62.
[36]张云涛,龚玲.数据挖掘原理与技术[M].北京:电子工业出版社,2004.12.
[37]肖勇,陈意云.用遗传算法构造决策树[J].计算机研究与发展,1998.35(1):49-52.
[38]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安交大出版社,2002,143-144.
[39]李军,郭耀煌.物流配送车辆优化调度理论与方法[M].北京:中国物资出版社,2001:78-85.
[40]张志艳.数据仓库在超市配送决策支持中的应用[D].辽宁:辽宁工程技术大学计算机应用技术系,2005.
[2] (美)Steve Hoberman著,贾爱霞,程耀译.数据建模[M].北京:机械工业出版社,2004:28-36
[3] Holland著.自然和人工系统中的适应性[M].1975.
[4]李敏强.遗传算法的基本理论与应用[M].北京:科学出版社,2003.
[5]郎茂祥,基于遗传算法的物流配送路径优化问题研究[J],中国公路学报.系统工程理论与实践,1999,22-25.
[6] Z.米凯利维茨,演化程序-遗传算法和数据编码的结合[M],北京:科学出版社,2000.
[7] Climaco J C,Alves M.Interactive Decision Support for Multiobjective Transportation Problem.European Journal of Operational Research[M],1993(65):58-67.
[8] Vignaux G A,Michalewicz Z.A Genetic Algorithm for the Linear Transportation Problem.IEEE Transportations on Systems,Man,and Cybernetics[M],1991(21):445-452.
[9] Michalewicz Z,Vignaux G A,Hobbs M.A Non-Standard Genetic Algorithm for the Nonlinear Transportation Problems.ORSA Journal on Computing[M], 1991,3(4):307-316.
[10]王日爽,徐兵等.应用动态规划.国防工业大学出版社[M],1987.7,第一版.
[11]陶谦坎.运筹学.西安交通大学出版社[M],1987.7,第一版.
[12]金升平.城市车辆配送线路设计的模型与算法[J].计算机工程与应用,2002,22 .
[13]谢如鹤,罗荣武,张得志等编著,物流系统规划原理与方法[M]中国物资出版社,2004.3.
[14]徐利群.物流运输路线选择与成本节约[J].上海管理科学,2004,4,49-50.
[15]林杰等,数据挖掘与OLAP理论与实务[M],北京:清华大学出版社,2003,第一版.
[16]郝渊晓,现代物流配送管理[M],中山大学出版社,2001.11.
[17]蒲春梅,王延清.物流运输中的隐性成本及其控制[J].商业时代,2005,15.
[18]李岩,申金升,沈昆.城市物流运输的可持续发展建议[J].山西交通科技杂志,2004,2.
[19]陈国良,王煦法,庄镇泉等,遗传算法及其应用[M],北京:人民邮电出版社,1996 .
[20] Mickey Yost.Data warehousing and decisinon support at the National Agricultural Statistics Service.Social Science Computer Review[M],2000.18(4):434-441.
[21]余达征,索丽生,史金松.基于数据仓库的数据挖掘技术及其在防洪调控智能决策支持系统(FCDIDSS)中的应用[J].水文,2001,21(2):5-8.
[22] Z.米凯利维茨,演化程序-遗传算法和数据编码的结合[M],北京:科学出版社,2000.
[23]王之泰.现代物流学[M].北京:中国物资出版社,2001. 45-70.
[24]王日爽,徐兵等.应用动态规划[M].国防工业大学出版社,1987.7,第一版.
[25]陶谦坎.运筹学[M].西安交通大学出版社,1987.7,第一版.
[26] W.H.Inmon.Building thd Data Warehouse[M].QED Technical publishing Group,1992 .
[27]邵举平.物流信息系统[M].北京:清华大学出版社,2005:56一78.
[28] R.Brachman and T.Anand.The Process of Knowledge Discovery in Databases:A Human Centered Approach.Advances in Knowledge Discovery and Data Mining,AAAI/MIT Press[M],1996.
[29]启明工作室.Visual C++ +SQL Server数据库应用系统开发与实例[M].北京人民邮电出版社,2004.07.
[30]黄维通.Visual C++面向对象与可视化程序设计习题解析与编程实例[M].北京:清华大学出版社,2003.12.
[31]刘耀宗.基于数据仓库的物流信息系统[D].南京:南京理工大学软件工程系,2004.
[32]周明,孙树栋.遗传算法原理及其应用[M].北京:国防工业出版社,1999.
[33]李敏强,寇纪松,林月,李书全.遗传算法的基本理论与应用[M].北京:科学出版社,2002.
[34]乐阳,龚健雅,Dijkstra最短路径算法的一种高效实现[J],武汉测绘科技大学学报,1999,Vol.24 No.3:209-212..
[35]辛香非,朱鑫.遗传算法的适应度函数研究[J].系统工程与电子技术,1998,(11):58-62.
[36]张云涛,龚玲.数据挖掘原理与技术[M].北京:电子工业出版社,2004.12.
[37]肖勇,陈意云.用遗传算法构造决策树[J].计算机研究与发展,1998.35(1):49-52.
[38]王小平,曹立明.遗传算法理论、应用与软件实现[M].西安交大出版社,2002,143-144.
[39]李军,郭耀煌.物流配送车辆优化调度理论与方法[M].北京:中国物资出版社,2001:78-85.
[40]张志艳.数据仓库在超市配送决策支持中的应用[D].辽宁:辽宁工程技术大学计算机应用技术系,2005.