船厂码头移泊作业及其相关问题研究
|
摘要
随着近几年我国船舶航运业的高速发展，国内船舶造修厂的业务量也快速增长，使得船厂码头停靠的船舶数量急剧增加，如何充分使用好船厂有限的码头资源的问题，在船厂生产日益增长的众多矛盾中也日渐凸现。作为船厂基本生产资料的码头，直接决定了船厂能够承接船舶数量的多少，所以船厂码头泊位的合理使用成为船厂解决这些矛盾的关键。船厂码头移泊作业问题，是直接关系到船厂生产作业的有序进行和船厂码头生产资源的科学使用问题，对船厂的科学管理和经济效益的提升都具有重要的意义，这也是本课题研究的基本出发点和现实基础。本课题运用“实践到理论，理论到实践”的方法，系统地研究了船厂码头移泊作业及其相关问题。
针对目前船厂码头移泊作业的研究不多和船厂靠经验解决存在不足的现状，本文基于动态规划和网络的相关理论，对船厂码头移泊作业及其相关问题进行了研究，并对解决问题的相关关键性技术进行了深入研究。本课题研究的主要内容包括：
1、研究了问题群在小规模情况下的动态网络规划法。分析了船厂移泊在小规模情况下的特点，建立起问题的动态规划递推式，然后把问题的动态规划形式映射为问题网络结构图的形式，通过问题网络结构图的逐步改进，最终获得了问题在对应网络图上的最优路径。本方法通过把移泊作业的动态规划过程转化为网络图的最优路径的搜寻，然后运用网络图的改进的方法来完成寻优任务。文章提出的方法极大地简化了问题动态演化的边界条件，避免了单纯规划法的递推循环的情况出现。
2、当移泊作业问题在大规模状况下，方案具有数学组合式特点，文章在智能算法的基础上，发展了问题的三种模型和解决办法。基于问题所构映射网络结构图，分析移船路径具有遍历特征，提出了问题的拓扑遍历算法。考虑船舶位置的逐步演进性，建立起问题的元胞自动机模型，利用蚁群算法，改进蚁群信息素的演进规则，达到最优路径快速搜寻的目的。研究问题形成的有向网络的马尔科夫特性，结合HNN网络神经理论，通过改造神经网络系统的能量方程的衰减函数，完成问题最优方案的求解。经实例应用，三种方法对于问题的求解具有高度的一致性，其收敛性是一致的，在计算机上计算时长都在0.1毫秒左右。问题的智能算法为大规模求解提供了快速、有效的方法。
3、文章解决了具有初始解的船厂码头移泊作业问题。文章运用五点变分的方法将问题给定的初始路径（具有的初始解）通过变分散射逐步将其改造为含有最优路径（最优解）的赋值网络图，然后从构造的网络图中搜寻问题的最优路径。文章将动态规划理论与变分理论充分结合，拓展了问题图的特性，方法突出了“由路径到图，又由图到路”的理念，同时在计算中，对变分的收敛标准采用边计算边校正的原则，这样可以保证搜寻到最优路径。对于某个已知方案通过该方法去快速优化该方案，是非常有效的。
4、论文研究了船厂码头移泊作业的相关问题。相关问题的研究是移泊问题的补充也是问题研究的基础。论文首先运用层次分析法给出了船厂码头船舶重要性排序的解决办法，运用线性规划方法解决了船厂生产任务与船厂的资源匹配问题。这些问题的研究，为船厂的码头船舶泊位的合理安排和资源的合理分配提供了理论依据和解决方法，使得本课题的研究得到了有机延伸。
本文选定船厂实际运作中具有典型性和代表性的系列规模数据实例，运用VC++编程实现了算例的分析计算，通过对计算结果和船厂实际操作进行了综合评价和比较，得出本课题研究所采用的方法具有很好的优选效果的结论。本文通过理论分析了船厂码头移泊作业问题的不同状况和不同特点，获得了问题的不同模型和解决方法，对于船厂科学管理其码头资源具有很大的积极意义。同时，文章发展了传统的动态规划理论和图论，把问题的寻优策略紧密结合起来，提出了多种有效的解决方法，对问题的软件化和进一步深入研究提供了更广的平台和坚实的基础。
With fast development of domestic shipping industry, shipbuilding and ship repairingmarket in China grows rapid accordingly. As a result, the number of vessels berthed inshipyard increased sharply. In this case, the issue of how to make full use of the wharfresources becomes even more outstanding among the more and more contradictions inshipyard production. Wharf, which is considered as the basic production resource of shipyard,affects shipyard’s undertaken capability directly. The reasonable management of the wharfberths becomes the key to solve these contradictions. The topic for shifting berth directlyrelates to the shipyard orderly production operation and scientific resources usage. Also, thistopic plays an important role in the scientific management of shipyard and economic benefitincrease. This is also the basic starting point and reality foundation. In this essay,“frompractice to the theory, using the theory to guide practice” is to be applied to research. Shiftingberth and the related works in shipyards are described systemically in this paper.
In view of shipyard’s limited experience in researching in berth shifting and theinsufficiency in solving shifting work by experience only currently, based on the foundationin dynamic programming and network related theory, this essay studied the shifting berth andits related work and the critical factor to solutions. The content of the subject are listed asbelow：
1. The essay studies the dynamic network planning for the case in small scale. Byanalyzing the character of berth shifting in small scale, the research set up the dynamicplanning recurrence and output as the problems network organization chart from dynamicprogramming. By gradual improvement for the problem network organization chart, the bestway for solving the problem in the network chart is obtained. This method could achieve themost optimized tasks via the network charts by transferring dynamic programming progressof berth shifting to searching most optimized path of network. The method presented in thischapter greatly simplifies the problem of dynamic evolution of boundary conditions, whichavoids recursive recycling in simple programming
2. In the condition of issues on a large scale, this paper has work out an intelligentsolution method towards that issue through program characterized by combined type. Basedon the different characteristics of that issue, this paper has studied out three models andsolution methods. Upon the issue mapping into Network structure characterized byperiodicity of ship path moving, evolutionary of ship location and Markov of directed network, the paper has put forward Topological Optimization, Ship Cellular Automata modeland HNN Neural Network Algorithm, and studied out the optimal decision under differentcircumstances. It is proved by practical application that three methods is highly consistentregarding solving that issue with consistent convergence. The intelligent solution method hasprovided a fast and effective way for issues on a large scale.
3. This paper has solved out the issue of shipyards Berth Shifting with initial solution.By using five variation method and variation scattered shoot, this paper had changed theinitial paths (with initial solution) into Assignment network diagram with optimal path（optimal solution）. With fully combination of theory of dynamic programming andvariation theory, this algorithm had developed the characteristic of problem graph. It is a veryeffective method for optimizing known program.
4. The essay focuses on the topic of Shifting Berth. The study of related topics is boththe supplement and base of the topic of Shifting Berth. In this essay, analytic hierarchyprocess (AHP) is applied for finding out the solution of importance sequencing of vessels forberth and linear programming method for the solution of balance between resources andproduction tasks. Highlight the concept of by the path to the map, also from the map to theroad. In the calculation, variational convergence criteria use the principle of calculation andcorrection. it can guarantee the search to optimal path. For a given method to fastoptimization of the programme, is very effective.
Based on typical and representative serial demonstrations in actual operation, bycomprehensive evaluation and comparison of the analysis calculation result in VC++programming and actual operation, the essay comes out with conclusion that the methodsapplied in this subject can achieve good and optimized effect. The essay have achieveddifferent models and solutions by analysis for various situation and features in Berth Shifting,which is very meaningful for managing berth resources scientifically in shipyard. At the sametime, by developing traditional dynamic planning theory and graph theory, combining theoptimization seaking strategy and raising several effective solutions, this essay have providedbroader platform and solid foundation for Software-oriented and further studies.
针对目前船厂码头移泊作业的研究不多和船厂靠经验解决存在不足的现状，本文基于动态规划和网络的相关理论，对船厂码头移泊作业及其相关问题进行了研究，并对解决问题的相关关键性技术进行了深入研究。本课题研究的主要内容包括：
1、研究了问题群在小规模情况下的动态网络规划法。分析了船厂移泊在小规模情况下的特点，建立起问题的动态规划递推式，然后把问题的动态规划形式映射为问题网络结构图的形式，通过问题网络结构图的逐步改进，最终获得了问题在对应网络图上的最优路径。本方法通过把移泊作业的动态规划过程转化为网络图的最优路径的搜寻，然后运用网络图的改进的方法来完成寻优任务。文章提出的方法极大地简化了问题动态演化的边界条件，避免了单纯规划法的递推循环的情况出现。
2、当移泊作业问题在大规模状况下，方案具有数学组合式特点，文章在智能算法的基础上，发展了问题的三种模型和解决办法。基于问题所构映射网络结构图，分析移船路径具有遍历特征，提出了问题的拓扑遍历算法。考虑船舶位置的逐步演进性，建立起问题的元胞自动机模型，利用蚁群算法，改进蚁群信息素的演进规则，达到最优路径快速搜寻的目的。研究问题形成的有向网络的马尔科夫特性，结合HNN网络神经理论，通过改造神经网络系统的能量方程的衰减函数，完成问题最优方案的求解。经实例应用，三种方法对于问题的求解具有高度的一致性，其收敛性是一致的，在计算机上计算时长都在0.1毫秒左右。问题的智能算法为大规模求解提供了快速、有效的方法。
3、文章解决了具有初始解的船厂码头移泊作业问题。文章运用五点变分的方法将问题给定的初始路径（具有的初始解）通过变分散射逐步将其改造为含有最优路径（最优解）的赋值网络图，然后从构造的网络图中搜寻问题的最优路径。文章将动态规划理论与变分理论充分结合，拓展了问题图的特性，方法突出了“由路径到图，又由图到路”的理念，同时在计算中，对变分的收敛标准采用边计算边校正的原则，这样可以保证搜寻到最优路径。对于某个已知方案通过该方法去快速优化该方案，是非常有效的。
4、论文研究了船厂码头移泊作业的相关问题。相关问题的研究是移泊问题的补充也是问题研究的基础。论文首先运用层次分析法给出了船厂码头船舶重要性排序的解决办法，运用线性规划方法解决了船厂生产任务与船厂的资源匹配问题。这些问题的研究，为船厂的码头船舶泊位的合理安排和资源的合理分配提供了理论依据和解决方法，使得本课题的研究得到了有机延伸。
本文选定船厂实际运作中具有典型性和代表性的系列规模数据实例，运用VC++编程实现了算例的分析计算，通过对计算结果和船厂实际操作进行了综合评价和比较，得出本课题研究所采用的方法具有很好的优选效果的结论。本文通过理论分析了船厂码头移泊作业问题的不同状况和不同特点，获得了问题的不同模型和解决方法，对于船厂科学管理其码头资源具有很大的积极意义。同时，文章发展了传统的动态规划理论和图论，把问题的寻优策略紧密结合起来，提出了多种有效的解决方法，对问题的软件化和进一步深入研究提供了更广的平台和坚实的基础。
With fast development of domestic shipping industry, shipbuilding and ship repairingmarket in China grows rapid accordingly. As a result, the number of vessels berthed inshipyard increased sharply. In this case, the issue of how to make full use of the wharfresources becomes even more outstanding among the more and more contradictions inshipyard production. Wharf, which is considered as the basic production resource of shipyard,affects shipyard’s undertaken capability directly. The reasonable management of the wharfberths becomes the key to solve these contradictions. The topic for shifting berth directlyrelates to the shipyard orderly production operation and scientific resources usage. Also, thistopic plays an important role in the scientific management of shipyard and economic benefitincrease. This is also the basic starting point and reality foundation. In this essay,“frompractice to the theory, using the theory to guide practice” is to be applied to research. Shiftingberth and the related works in shipyards are described systemically in this paper.
In view of shipyard’s limited experience in researching in berth shifting and theinsufficiency in solving shifting work by experience only currently, based on the foundationin dynamic programming and network related theory, this essay studied the shifting berth andits related work and the critical factor to solutions. The content of the subject are listed asbelow：
1. The essay studies the dynamic network planning for the case in small scale. Byanalyzing the character of berth shifting in small scale, the research set up the dynamicplanning recurrence and output as the problems network organization chart from dynamicprogramming. By gradual improvement for the problem network organization chart, the bestway for solving the problem in the network chart is obtained. This method could achieve themost optimized tasks via the network charts by transferring dynamic programming progressof berth shifting to searching most optimized path of network. The method presented in thischapter greatly simplifies the problem of dynamic evolution of boundary conditions, whichavoids recursive recycling in simple programming
2. In the condition of issues on a large scale, this paper has work out an intelligentsolution method towards that issue through program characterized by combined type. Basedon the different characteristics of that issue, this paper has studied out three models andsolution methods. Upon the issue mapping into Network structure characterized byperiodicity of ship path moving, evolutionary of ship location and Markov of directed network, the paper has put forward Topological Optimization, Ship Cellular Automata modeland HNN Neural Network Algorithm, and studied out the optimal decision under differentcircumstances. It is proved by practical application that three methods is highly consistentregarding solving that issue with consistent convergence. The intelligent solution method hasprovided a fast and effective way for issues on a large scale.
3. This paper has solved out the issue of shipyards Berth Shifting with initial solution.By using five variation method and variation scattered shoot, this paper had changed theinitial paths (with initial solution) into Assignment network diagram with optimal path（optimal solution）. With fully combination of theory of dynamic programming andvariation theory, this algorithm had developed the characteristic of problem graph. It is a veryeffective method for optimizing known program.
4. The essay focuses on the topic of Shifting Berth. The study of related topics is boththe supplement and base of the topic of Shifting Berth. In this essay, analytic hierarchyprocess (AHP) is applied for finding out the solution of importance sequencing of vessels forberth and linear programming method for the solution of balance between resources andproduction tasks. Highlight the concept of by the path to the map, also from the map to theroad. In the calculation, variational convergence criteria use the principle of calculation andcorrection. it can guarantee the search to optimal path. For a given method to fastoptimization of the programme, is very effective.
Based on typical and representative serial demonstrations in actual operation, bycomprehensive evaluation and comparison of the analysis calculation result in VC++programming and actual operation, the essay comes out with conclusion that the methodsapplied in this subject can achieve good and optimized effect. The essay have achieveddifferent models and solutions by analysis for various situation and features in Berth Shifting,which is very meaningful for managing berth resources scientifically in shipyard. At the sametime, by developing traditional dynamic planning theory and graph theory, combining theoptimization seaking strategy and raising several effective solutions, this essay have providedbroader platform and solid foundation for Software-oriented and further studies.
引文
[1]黄勇.港口企业发展战略的实证研究[D].北京交通大学,2007.
[2]陈宁.港口运营结构优化理论、方法与实证研究[D].武汉理工大学,2006.
[3]蔡芸,张艳伟.集装箱码头泊位分配的仿真优化方法[J].中国工程机械学报,2006,4(2),228-232.
[4]李平.港口泊位分配与装卸桥调度系统的建模与优化[D].天津理工大学,2007.
[5]周强,盛伟,吴澎.柔性靠泊方式的集装箱码头马尔可夫过程研究[J].水运工程,2005,(8),15-17.
[6]张煜,王少梅.基于遗传算法的泊位连续化动态调度研究[J].系统仿真学报,2007,19(10),2161-2164.
[7]吕威.基于柔性靠泊系统的集装箱码头岸线规划研究[D].大连理工大学,2008.
[8]杨海东.港口泊位系统仿真求解泊位分配问题[D].天津大学,2006.
[9]韩骏,孙晓娜,靳志宏.集装箱码头泊位与岸桥协调调度优化[J].大连海事大学学报,2008,34(2),117-121.
[10]于蒙,王少梅.基于多Agent的集装箱码头生产调度系统建模研究[J].武汉理工大学学报（交通科学与工程版）,2007,31(3),494-494.
[11]李强,杨春霞,王诺等.集装箱码头泊位生产调度均衡优化[J].沈阳建筑大学学报（自然科学版）,2008,24(6),1132-1136.
[12]张煜.面向集装箱码头的仿真技术和优化方法研究[D].武汉理工大学,2007.
[13]罗旭.散粮码头PLC控制系统的设计与实现[D].武汉理工大学,2008.
[14]李云军.封闭式堆场煤炭码头物流系统建模与分析[D].武汉理工大学,2007.
[15]吴冕.秦皇岛港煤炭物流体系建设研究[D].武汉理工大学,2007.
[16]黄鑫乐.基于遗传算法的铁矿石码头泊位配置仿真优化方法的应用研究[D].上海海事大学,2007.
[17]黄国梁.散货码头物流系统建模与仿真[D].武汉理工大学,2007.
[18]王建强.散货件杂货码头装卸工艺流程仿真研究[D].华南理工大学,2005.
[19]吴旭填.广州港拖轮配置分析[D].上海海事大学,2005.
[20]王磊.港口资源优化关键技术的研究[D].华南理工大学,2005.
[21]刘冰.遗传算法及其在引航排班中的应用研究[D].大连海事大学,2007.
[22]缪立新,乌英.遗传算法在集装箱码头综合调度中的应用[J].集装箱化,2008,19(7),18-21.
[23]张煜,李文锋,严新平.集装箱作业系统一体化研究调度综述[J].武汉理工大学学报,2011,23(1),11-14.
[24]李平,孙俊清,韩梅.泊位调度问题的GATS混合优化策略[J].天津理工大学学报,2006,22(4),58-61.
[25]张燕涛.基于遗传算法的泊位调度问题优化研究及仿真[D].武汉理工大学,2005.
[26]林丹青.基于BPR的件杂散货码头调度信息系统的研究[D].华中科技大学,2006.
[27]程栋文.基于Witness的船舶调度与港口资源配置问题的研究[D].天津工业大学,2007.
[28]王中华.基于遗传算法的港口船舶调度优化问题研究[D].上海海事大学,2007.
[29] IMAI A, NISHIMURA E, PAPADIMITRIOU S. The dynamic berth allocation problemfor a container port[J]. Transportation Research,2001,B35,401-417.
[30] NISHIMURA E, IMAI A. Berth allocation planning in the public berth system byGA[J]. European Journal of Operation Research,2001,(4).282-292.
[31] IMAI A, NISHIMURA E. Berth allocation with service priority[J]. TransportationResearch,2003,Part B37,437-547.
[32] Kap Hwan Kim, Kyung Chan Moon. Berth scheduling by simulated annealing[J].Transportation Research: Part B,2003,37,541-560.
[33] Etsuko Nishimura, Akio Imai, Stratos Papadimitriou. Berth allocation planning in thepublic berth system by genetic algorithms[J]. European Journal of OperationalResearch,2001,131,282-292.
[34] Duinkerken MB, Evers JJM, Ottjes JA. Improving Quay Transport on AutomatedContainer Terminals[R]. Proceedings of the IASTED International Conference AppliedSimulated on Modeling(ASM2002),2002.
[35] Veeke HPM, Ottjes JA. A Generic Simulation Model for System of ContainerTerminals[R]. Proceeding of the16thEuropean Simulation Multiconference(ESM2002).
[36] IMAI A, NAGAIWA K,CHEN W T. Efficient planning of berth allocation for containerterminals in Asia[J]. Journal of Advanced Transportation,1997,31,75-94.
[37] Park Y M, Kim K M. A Scheduling method for berth and quay cranes[J]. OR Spectrum,2003,25,1-23.
[38] Guan Yongpei, Xiao Wenqiang, Cheung R K, et al. A Multiprocessor task schedulingmodel for berth allocation: heuristic and worst–case analysis[J]. Operations ResearchLetters,2002,30,343-350.
[39] Andrew L. The berth planning problem[J]. Operations Research Letters,1998,22,105-110.
[40] Li C, Cai X, Lee C Y. Scheduling with multiple-job-on-one-processor pattern[J]. IIETransactions,1998,30,433-445.
[41]蔡芸.同时求解泊位分配及岸桥调度问题的仿真优化方法[A].2005年全国博士生学术论坛--交通运输工程学科论文集[C].2005.
[42]朱云龙,罗焕佐,薛劲松等.港口业船舶优化调度模型设计[J].高技术通讯,2000,10(7),79-81.
[43]谢尘,严伟,苌道方.集装箱码头泊位分配模型与仿真研究[A].2008仿真科学与技术青年学术论坛论文集[C].2008.
[44]吕威.基于柔性靠泊系统的集装箱码头岸线规划研究[D].大连理工大学,2008.
[45]陈璐.集装箱码头装卸作业调度模型及优化算法研究[D].上海交通大学,2006.
[46]王学锋.基于枢纽港的大型集装箱船营运经济性研究[D].上海交通大学,2007.
[47]王世凯.集装箱码头泊位资源优化配置研究[D].上海海事大学,2007.
[48]于蒙.基于多Agent的集装箱码头生产调度系统的研究[D].武汉理工大学,2007.
[49]张婕姝.集装箱码头生产调度优化研究[D].上海海事大学,2006.
[50]韩晓龙.集装箱港口装卸作业资源配置研究[D].上海海事大学,2005.
[51]王素明.基于蚁群算法的港口调度问题研究[D].辽宁科技大学,2008.
[52] Imai A, Sun X, NISHIMURA E, et al. Berth allocation in a container port: using acontinuous location space approach[J]. Transportation Research,2005,B39,199-221.
[53] Imai A, Nishimuta E, Hattori M. Berth allocation at indented berths formega-containerships[J]. Europen Journal of Operational Research,2006(3).
[54] Kuang H B, Wang J,"Research on Single Port Investment Decision-makingOptimization Model Based on Real Options", International Journal of Digital ContentTechnology and its Applications, AICIT, Vol.4, No.9, pp.234-241,2010.
[55] Legatto P, Mazza R M. Berth planning and resources optimization at a containerterminal via discrate event simulation[J]. European Journal of Operational Research,2001,133(3),537-547.
[56] KIM K H, MOON K C. Berth scheduling by simulated annealing[J]. TransportationResearch,2003,B37,541-560.
[57]孟昭源.我国大中型干船坞工艺现状及存在问题[J].水运工程,1983,15-20.
[58]孙树俭.提高修船船坞利用率的几点建议[J].中国修船,2006,19(2),13-14.
[59]杨新昆.船舶修理业绩非凡[J].中国修船企业,2009,(2),1-5.
[60]杨新昆.渡过金融危机的寒流迎接修船回暖的春天[J].中国修船企业,2010,(2),25-30.
[61]杨新昆.修船走出低谷尚需时日[J].中国修船企业,2011,(3),2-4.
[62] Chen, zhiping, and Xu, zhongben (2005)."Computer mathematics," Science press, pp51-59.
[63] Jianping Chen, Jiawei Ye. The Computational Complexity of Ship Shifting andBerthing Scheduling at Dockyard[C]. CCDC-2011.
[64] Han, zhonggeng (2005)."Mathematical modeling methodologies and application,"Higher education press, pp220-225
[65]缪立新,乌英.遗传算法在集装箱码头综合调度中的应用[J].集装箱化,2008,19(7),18-21.
[66]张婕姝.港口生产调度仿真模型[J].上海海事大学学报,2005,26(2),42-46.
[67]鲁子爱.排队论在港口规划中的应用[J].水运工程,1997(8),11-15.
[68]李大伟.基于系统仿真的港区门口数量与泊位数量的研究[D].大连理工大学,2007.
[69]杨兴晏.沿海港口集装箱码头合理的泊位利用率分析[J].港工技术,2004(3),5-7.
[70] M. R. Garey and D. S. Johnson,"Computers and Intractibility: A Guide to the Theory ofN P-Completeness," Freeman, pp60-62,1979.
[71]邢文训,谢金星.现代优化计算方法[M].清华大学出版社，2005，pp81-83
[72]马玉玲.浅谈求解组合优化问题的几种近似算法[J].电脑知识与技术，2008，(9),2819-2820.
[73] C. H. Papadimirtriou,"Combinatiorial Opimization: Algorithms and Complexity,"New Jersey: Prentice-Hall Inc, pp408-409,1982
[74] Gray C., Ping Zhang (2007)."Introduction to Graph Theory," Posts&Telecom press,pp41-44
[75] J. M. Keil,"Finding Hamiltonian circuits in interval graphs," Inform. Process Lett,vol20: pp201-206,1985.
[76]高随祥.图论与网络流理论[M].高等教育出版社,2009,122-139.
[77] Garey M.R., Johnson D.S. Computers and Intractibility: A guide to the Theory of NPC,Freeman,1979,60-62.
[78] G Reinelt. A Traveling Salesman Problem Library, ORSA J[J]. Computer,1991(3),376-384.
[79] Jianping Chen, Jiawei Ye. Modeling for Ship Shifting and Berthing Jobs Scheduling atDockyard[C]. ISOPE-PACOMS-2010,325-332.
[80]沙梅.集装箱码头物流运营系统通用性建模与仿真[M].上海交通大学出版社，2009,10-13.
[81] Mark M. Meerschaert. Mathematical Modeling[M].机械工业出版社，2009,89-103.
[82] P. Goulden and D. M. Jackson. Combinatorial Enumeration[M]. Wisley-Interscience,1983.
[83] J.H. Van Lint, R.M. Wilson. A Course in Combinatorics[M].机械工业出版社，2007.
[84] Jianping Chen, Jiawei Ye. The Graph Model and the Dijkstra Algorithmic of the ShipShifting and Berthing Operation at the Dockyard[C]. ISOPE-2011, Vol.3,1207-1214.
[85] Chartland G., Jackson M. S., Oellermann O. R., Ruiz S. and Saba F. Irregularnetworks[M]. Congress. Numeran,1988,64,197-210.
[86] Gary Chartrand, Ping Zhang.图论导引[M].人民邮电出版社，2007,41-44.
[87] Jianping Chen, Jiawei Ye. Modeling of Ship shifting Scheduling at Dockyard andResearch of it’s Algorithmic[J]. Journal of Information&Computational Science,2011,8(13):2851-2864.
[88]张贤达.矩阵分析与应用[M].清华大学出版社，2004,100-104.
[89] Jianping Chen, Jiawei Ye. Shipyard Ship Shifting Scheduling Problem with DynamicProgramming Model and its Five-Point Approach Method[C]. ICTE-2011.
[90] Jianping Chen, Jiawei Ye. Modeling of Shipyard Ship Shifting Scheduling Problem andits Five-point Approach Method based on Dynamic Programming[J]. InternationalJournal of Digital Content Technology and its Applications,2011,5(10):211-218.
[91]黎日松.f1×f2×L×fn及f n的拓扑遍历性[J].南开大学学报（自然科学版）,2009,42(1):28-33.
[92]王爱虎,杨曼丽.复杂产品开发项目系统化管理方法[J].工业工程与管理,2005,10(3):7-21.
[93]方炜,欧立雄.多项目环境下新产品研发项目资源分配问题研究[J].管理工程学报,2005,19(S):6-10.
[94]寿涌毅.资源约束下多项目调度的迭代算法[J].浙江大学学报(工学版),2004,38(8):1095-1099
[95] BRUCKER P, ANDREAS D, ROLF M, el at. Resource constrained project scheduling:notation, classification, models and methods[J]. European Journal of OperationResearch,1999,112(1):3-41.
[96] LOVA A, MAROTO C, TORM OSP. A multicriteria heuristic method to improveresource allocation in multiproject scheduling[J]. European Journal of OperationalResearch,2000,127(2):408-424.
[97]邓林义,林焰,陈明,金朝光,游前文.计算机集成制造系统[J].2007,13(3):431-435
[98]金朝光.船舶建造工程管理及供应链一体化研究[D].大连:大连理工大学,2002.
[99] Nagel K, Rickert M. Parallel Implementation of the TRANSIMS Micro-Simulation[J].Parallel Comput.,2001,27:1611-1639.
[100]雷德明，严新平.多目标智能优化算法及其应用[M].科学出版社，2009
[101]焦李成，尚荣华，马文萍.多目标优化免疫算法理论和应用[M].科学出版社，2010
[102]翁妙凤.基于并行进化规划的Job Shop动态调度策略[J].小型微型计算机系统,2000,21(6)
[103] Lova A, Maroto C, Tormosp. A multicriteria heuristic method to improve resourceallocation in multiproject scheduling[J]. European Journal of Operational Research,2000,127(2),408-424.
[104]孟鑫,范钦杰,王宏仁.集值离散动力系统的拓扑遍历性与链遍历性[J].2011,29(1):20-22.
[105]杨润生.拓扑遍历映射[J].数学学报,2001,44(6):1063-1068.
[106] KOLISCH R. Seri al and parallel resource constrained project scheduling methodsrevisited: theory and computation[J]. European Journal of Operational Research,1996,90(2):320-333.
[107] MIAO Z, YASSINE A. Task scheduling of parallel development projects using geneticalgorithms[C]//Proceedings of2004American Society of Mechanical EngineersDesign Automation Conference. Chicago, Ill., USA: ASME,2004:1-11.
[108]陈根方,张立印.基于串行计算的排序算法实证分析[J].杭州师范大学学报,2010,9(2),135-138.
[109]平萍.元胞自动机原理及其在密码学的应用研究[D].南京理工大学,2009.
[110]郑大钟,赵千川.离散事件动态系统[M].清华大学出版社,2000,25-29.
[111]孔宪娟,高自友,李克平.考虑近邻和次近邻车辆影响的单车道元胞自动机模型[J].北京交通大学学报,2006,30(6):11-15.
[112]陈健,雷洪,田帅,赫冀成.基于排队论的元胞自动机在港口交通流的研究[J].沈阳师范大学学报（自然科学版）,2009,27(3):319-322.
[113]李松，张杰，贺国光.基于元胞自动机模型的交通流混沌仿真研究[J].计算机工程与应用,2007(32):20-22.
[114]张晋,王慧,李平.基于一维元胞自动机的可变时距跟车模型[J].浙江大学学报,2006,40(2):233-237.
[115] EMMERICH H, RANK E. An improved cellular automaton model for traffic flowsimulation[J]. Physica A,1997,234(3):676-686.
[116]吕晓阳,孔令江,刘慕仁.一维元胞自动机随机交通流模型的宏观方程分析[J].物理学报,2001,50(7):1255-1259.
[117] Kareem, E.I.A., Jantan, A.,"An approach for monitoring pedestrian by usingmulti-connect architected MCA associative memory", International Journal of DigitalContent Technology and its Applications, vol.3, no.2, pp.75-82,2009.
[118]董力耘,薛郁,戴世强.基于跟车思想的一维元胞自动机交通流模型[J].应用数学与力学,2002,23(4):331-337.
[119]张杰,贺国光.基于一维元胞自动机模型的交通流混沌研究[J].武汉理工大学学报（交通科学与工程版）,2009,33(1):33-36.
[120] Jianping Chen, Jiawei Ye. Research of Cellular Automata Model of Dockyard ShipShifting Scheduling Problem and Its Ant Colony AlgorithmJ]. International Journal ofAdvancements in Computing Technology,2012,4(1):312-319.
[121]马良，朱刚，宁爱兵.蚁群优化算法[M].科学出版社，2008
[122] FIEMS D, MAERTENS T, BRUNEEL H. Qeueueing systems with different types ofserver interruptions[J] European Journal of Operational Research,2008,188(3):838-845.
[123]沈艳.神经网络理论研究及在舰船运动预报中的应用[D].哈尔滨工程大学,2005.
[124]周开利，康耀红.神经网络模型及其MATLAB仿真程序设计[M].清华大学出版社，2005
[125] Fieldsend J E, Singh S. Pareto evolutionary neural netwoerks[J]. IEEE Transactions onNeural Networks,2005,16(2),338-354.
[126]马铭,周春光,张利彪.一种优化模糊神经网络的多目标微粒群算法[J].计算机研究与发展,2006,43(12),2104-2109.
[127] Castillo P A. Optimization de perceptrones multicapa mediante algoritmos evolutivos[J].Ph. D. Dissertation, Univ. Granada, Spain,2000.
[128]王岩,周春光,黄艳新.基于最小不确定神经网络的茶味觉信号识别[J].计算机研究与发展,2005,42(1),66-71.
[129] Deigado M, Pegalajar M C. A mult-objective genetic algorithm for obtaining theoptimal size of a recurrent neural network for grammatical inference[J]. PatternRecognition,2005,38,1444-1456.
[130]郎茂祥.配送车辆优化调度模型与算法[M].电子工业出版社，2009,146-151.
[131]徐杰,黄德先.基于混合粒子群算法的多目标车辆路径研究[J].计算机集成制造系统,2007,13(3),573-579.
[132]肖健梅,李军军,王锡准.求解车辆路径问题的改进粒子群优化算法[J].计算机集成制造系统,2005,11(4),577-581.
[133] Christian P. A simple and effective evolutionary algorithm for the vechicle routingproblem[J]. Computers and Operation Research,2004,31(12),1985-2002.
[134]郎茂祥.多配送中心车辆调度问题的模型与算法研究[J].交通运输系统工程与信息,2006,6(5),
[135]张炯,郎茂祥.有时间窗配送车辆调度问题的禁忌搜索算法[J].北方交通大学学报,2004,28(2),
[136]谢秉磊,郭耀煌,郭强.动态车辆路径问题:现状与展望[J].系统工程理论方法应用,2002,11(2),
[137]肖增军,李军.动态网络车辆路径问题[J].系统工程,2004,22(7),
[138] Jianping Chen, Jiawei Ye. Modeling of Shipyard Ship Shifting Scheduling Problem andits Five-point Approach Method based on Dynamic Programming[J]. InternationalJournal of Digital Content Technology and its Applications,2011,5(10):211-218.
[139]陈建平.船舶修理任务和资源合理配置的线性规划分析[J].船海工程,2010,39(195):41-43.
[140]周洋.国内修船单船管理模式发展概述[J].中国修船,2008,21(6),5-8.
[141]陈建平.船舶修理生产资源的合理分配[J].中国修船,2009,22(135):6-8.
[142]王庚，王敏生.现代数学建模方法[M].科学出版社，2008
[143] Steven J.Leon. Linear Algebra with Applications[M].机械工业出版社，2009
[144]陈建平.船舶常规修理和改装的经济性分析[J].江苏船舶，2008,25(6):25-27.
[145]任玉杰.数值分析及其MATLAB实现[M].高等教育出版社，2007
[2]陈宁.港口运营结构优化理论、方法与实证研究[D].武汉理工大学,2006.
[3]蔡芸,张艳伟.集装箱码头泊位分配的仿真优化方法[J].中国工程机械学报,2006,4(2),228-232.
[4]李平.港口泊位分配与装卸桥调度系统的建模与优化[D].天津理工大学,2007.
[5]周强,盛伟,吴澎.柔性靠泊方式的集装箱码头马尔可夫过程研究[J].水运工程,2005,(8),15-17.
[6]张煜,王少梅.基于遗传算法的泊位连续化动态调度研究[J].系统仿真学报,2007,19(10),2161-2164.
[7]吕威.基于柔性靠泊系统的集装箱码头岸线规划研究[D].大连理工大学,2008.
[8]杨海东.港口泊位系统仿真求解泊位分配问题[D].天津大学,2006.
[9]韩骏,孙晓娜,靳志宏.集装箱码头泊位与岸桥协调调度优化[J].大连海事大学学报,2008,34(2),117-121.
[10]于蒙,王少梅.基于多Agent的集装箱码头生产调度系统建模研究[J].武汉理工大学学报（交通科学与工程版）,2007,31(3),494-494.
[11]李强,杨春霞,王诺等.集装箱码头泊位生产调度均衡优化[J].沈阳建筑大学学报（自然科学版）,2008,24(6),1132-1136.
[12]张煜.面向集装箱码头的仿真技术和优化方法研究[D].武汉理工大学,2007.
[13]罗旭.散粮码头PLC控制系统的设计与实现[D].武汉理工大学,2008.
[14]李云军.封闭式堆场煤炭码头物流系统建模与分析[D].武汉理工大学,2007.
[15]吴冕.秦皇岛港煤炭物流体系建设研究[D].武汉理工大学,2007.
[16]黄鑫乐.基于遗传算法的铁矿石码头泊位配置仿真优化方法的应用研究[D].上海海事大学,2007.
[17]黄国梁.散货码头物流系统建模与仿真[D].武汉理工大学,2007.
[18]王建强.散货件杂货码头装卸工艺流程仿真研究[D].华南理工大学,2005.
[19]吴旭填.广州港拖轮配置分析[D].上海海事大学,2005.
[20]王磊.港口资源优化关键技术的研究[D].华南理工大学,2005.
[21]刘冰.遗传算法及其在引航排班中的应用研究[D].大连海事大学,2007.
[22]缪立新,乌英.遗传算法在集装箱码头综合调度中的应用[J].集装箱化,2008,19(7),18-21.
[23]张煜,李文锋,严新平.集装箱作业系统一体化研究调度综述[J].武汉理工大学学报,2011,23(1),11-14.
[24]李平,孙俊清,韩梅.泊位调度问题的GATS混合优化策略[J].天津理工大学学报,2006,22(4),58-61.
[25]张燕涛.基于遗传算法的泊位调度问题优化研究及仿真[D].武汉理工大学,2005.
[26]林丹青.基于BPR的件杂散货码头调度信息系统的研究[D].华中科技大学,2006.
[27]程栋文.基于Witness的船舶调度与港口资源配置问题的研究[D].天津工业大学,2007.
[28]王中华.基于遗传算法的港口船舶调度优化问题研究[D].上海海事大学,2007.
[29] IMAI A, NISHIMURA E, PAPADIMITRIOU S. The dynamic berth allocation problemfor a container port[J]. Transportation Research,2001,B35,401-417.
[30] NISHIMURA E, IMAI A. Berth allocation planning in the public berth system byGA[J]. European Journal of Operation Research,2001,(4).282-292.
[31] IMAI A, NISHIMURA E. Berth allocation with service priority[J]. TransportationResearch,2003,Part B37,437-547.
[32] Kap Hwan Kim, Kyung Chan Moon. Berth scheduling by simulated annealing[J].Transportation Research: Part B,2003,37,541-560.
[33] Etsuko Nishimura, Akio Imai, Stratos Papadimitriou. Berth allocation planning in thepublic berth system by genetic algorithms[J]. European Journal of OperationalResearch,2001,131,282-292.
[34] Duinkerken MB, Evers JJM, Ottjes JA. Improving Quay Transport on AutomatedContainer Terminals[R]. Proceedings of the IASTED International Conference AppliedSimulated on Modeling(ASM2002),2002.
[35] Veeke HPM, Ottjes JA. A Generic Simulation Model for System of ContainerTerminals[R]. Proceeding of the16thEuropean Simulation Multiconference(ESM2002).
[36] IMAI A, NAGAIWA K,CHEN W T. Efficient planning of berth allocation for containerterminals in Asia[J]. Journal of Advanced Transportation,1997,31,75-94.
[37] Park Y M, Kim K M. A Scheduling method for berth and quay cranes[J]. OR Spectrum,2003,25,1-23.
[38] Guan Yongpei, Xiao Wenqiang, Cheung R K, et al. A Multiprocessor task schedulingmodel for berth allocation: heuristic and worst–case analysis[J]. Operations ResearchLetters,2002,30,343-350.
[39] Andrew L. The berth planning problem[J]. Operations Research Letters,1998,22,105-110.
[40] Li C, Cai X, Lee C Y. Scheduling with multiple-job-on-one-processor pattern[J]. IIETransactions,1998,30,433-445.
[41]蔡芸.同时求解泊位分配及岸桥调度问题的仿真优化方法[A].2005年全国博士生学术论坛--交通运输工程学科论文集[C].2005.
[42]朱云龙,罗焕佐,薛劲松等.港口业船舶优化调度模型设计[J].高技术通讯,2000,10(7),79-81.
[43]谢尘,严伟,苌道方.集装箱码头泊位分配模型与仿真研究[A].2008仿真科学与技术青年学术论坛论文集[C].2008.
[44]吕威.基于柔性靠泊系统的集装箱码头岸线规划研究[D].大连理工大学,2008.
[45]陈璐.集装箱码头装卸作业调度模型及优化算法研究[D].上海交通大学,2006.
[46]王学锋.基于枢纽港的大型集装箱船营运经济性研究[D].上海交通大学,2007.
[47]王世凯.集装箱码头泊位资源优化配置研究[D].上海海事大学,2007.
[48]于蒙.基于多Agent的集装箱码头生产调度系统的研究[D].武汉理工大学,2007.
[49]张婕姝.集装箱码头生产调度优化研究[D].上海海事大学,2006.
[50]韩晓龙.集装箱港口装卸作业资源配置研究[D].上海海事大学,2005.
[51]王素明.基于蚁群算法的港口调度问题研究[D].辽宁科技大学,2008.
[52] Imai A, Sun X, NISHIMURA E, et al. Berth allocation in a container port: using acontinuous location space approach[J]. Transportation Research,2005,B39,199-221.
[53] Imai A, Nishimuta E, Hattori M. Berth allocation at indented berths formega-containerships[J]. Europen Journal of Operational Research,2006(3).
[54] Kuang H B, Wang J,"Research on Single Port Investment Decision-makingOptimization Model Based on Real Options", International Journal of Digital ContentTechnology and its Applications, AICIT, Vol.4, No.9, pp.234-241,2010.
[55] Legatto P, Mazza R M. Berth planning and resources optimization at a containerterminal via discrate event simulation[J]. European Journal of Operational Research,2001,133(3),537-547.
[56] KIM K H, MOON K C. Berth scheduling by simulated annealing[J]. TransportationResearch,2003,B37,541-560.
[57]孟昭源.我国大中型干船坞工艺现状及存在问题[J].水运工程,1983,15-20.
[58]孙树俭.提高修船船坞利用率的几点建议[J].中国修船,2006,19(2),13-14.
[59]杨新昆.船舶修理业绩非凡[J].中国修船企业,2009,(2),1-5.
[60]杨新昆.渡过金融危机的寒流迎接修船回暖的春天[J].中国修船企业,2010,(2),25-30.
[61]杨新昆.修船走出低谷尚需时日[J].中国修船企业,2011,(3),2-4.
[62] Chen, zhiping, and Xu, zhongben (2005)."Computer mathematics," Science press, pp51-59.
[63] Jianping Chen, Jiawei Ye. The Computational Complexity of Ship Shifting andBerthing Scheduling at Dockyard[C]. CCDC-2011.
[64] Han, zhonggeng (2005)."Mathematical modeling methodologies and application,"Higher education press, pp220-225
[65]缪立新,乌英.遗传算法在集装箱码头综合调度中的应用[J].集装箱化,2008,19(7),18-21.
[66]张婕姝.港口生产调度仿真模型[J].上海海事大学学报,2005,26(2),42-46.
[67]鲁子爱.排队论在港口规划中的应用[J].水运工程,1997(8),11-15.
[68]李大伟.基于系统仿真的港区门口数量与泊位数量的研究[D].大连理工大学,2007.
[69]杨兴晏.沿海港口集装箱码头合理的泊位利用率分析[J].港工技术,2004(3),5-7.
[70] M. R. Garey and D. S. Johnson,"Computers and Intractibility: A Guide to the Theory ofN P-Completeness," Freeman, pp60-62,1979.
[71]邢文训,谢金星.现代优化计算方法[M].清华大学出版社，2005，pp81-83
[72]马玉玲.浅谈求解组合优化问题的几种近似算法[J].电脑知识与技术，2008，(9),2819-2820.
[73] C. H. Papadimirtriou,"Combinatiorial Opimization: Algorithms and Complexity,"New Jersey: Prentice-Hall Inc, pp408-409,1982
[74] Gray C., Ping Zhang (2007)."Introduction to Graph Theory," Posts&Telecom press,pp41-44
[75] J. M. Keil,"Finding Hamiltonian circuits in interval graphs," Inform. Process Lett,vol20: pp201-206,1985.
[76]高随祥.图论与网络流理论[M].高等教育出版社,2009,122-139.
[77] Garey M.R., Johnson D.S. Computers and Intractibility: A guide to the Theory of NPC,Freeman,1979,60-62.
[78] G Reinelt. A Traveling Salesman Problem Library, ORSA J[J]. Computer,1991(3),376-384.
[79] Jianping Chen, Jiawei Ye. Modeling for Ship Shifting and Berthing Jobs Scheduling atDockyard[C]. ISOPE-PACOMS-2010,325-332.
[80]沙梅.集装箱码头物流运营系统通用性建模与仿真[M].上海交通大学出版社，2009,10-13.
[81] Mark M. Meerschaert. Mathematical Modeling[M].机械工业出版社，2009,89-103.
[82] P. Goulden and D. M. Jackson. Combinatorial Enumeration[M]. Wisley-Interscience,1983.
[83] J.H. Van Lint, R.M. Wilson. A Course in Combinatorics[M].机械工业出版社，2007.
[84] Jianping Chen, Jiawei Ye. The Graph Model and the Dijkstra Algorithmic of the ShipShifting and Berthing Operation at the Dockyard[C]. ISOPE-2011, Vol.3,1207-1214.
[85] Chartland G., Jackson M. S., Oellermann O. R., Ruiz S. and Saba F. Irregularnetworks[M]. Congress. Numeran,1988,64,197-210.
[86] Gary Chartrand, Ping Zhang.图论导引[M].人民邮电出版社，2007,41-44.
[87] Jianping Chen, Jiawei Ye. Modeling of Ship shifting Scheduling at Dockyard andResearch of it’s Algorithmic[J]. Journal of Information&Computational Science,2011,8(13):2851-2864.
[88]张贤达.矩阵分析与应用[M].清华大学出版社，2004,100-104.
[89] Jianping Chen, Jiawei Ye. Shipyard Ship Shifting Scheduling Problem with DynamicProgramming Model and its Five-Point Approach Method[C]. ICTE-2011.
[90] Jianping Chen, Jiawei Ye. Modeling of Shipyard Ship Shifting Scheduling Problem andits Five-point Approach Method based on Dynamic Programming[J]. InternationalJournal of Digital Content Technology and its Applications,2011,5(10):211-218.
[91]黎日松.f1×f2×L×fn及f n的拓扑遍历性[J].南开大学学报（自然科学版）,2009,42(1):28-33.
[92]王爱虎,杨曼丽.复杂产品开发项目系统化管理方法[J].工业工程与管理,2005,10(3):7-21.
[93]方炜,欧立雄.多项目环境下新产品研发项目资源分配问题研究[J].管理工程学报,2005,19(S):6-10.
[94]寿涌毅.资源约束下多项目调度的迭代算法[J].浙江大学学报(工学版),2004,38(8):1095-1099
[95] BRUCKER P, ANDREAS D, ROLF M, el at. Resource constrained project scheduling:notation, classification, models and methods[J]. European Journal of OperationResearch,1999,112(1):3-41.
[96] LOVA A, MAROTO C, TORM OSP. A multicriteria heuristic method to improveresource allocation in multiproject scheduling[J]. European Journal of OperationalResearch,2000,127(2):408-424.
[97]邓林义,林焰,陈明,金朝光,游前文.计算机集成制造系统[J].2007,13(3):431-435
[98]金朝光.船舶建造工程管理及供应链一体化研究[D].大连:大连理工大学,2002.
[99] Nagel K, Rickert M. Parallel Implementation of the TRANSIMS Micro-Simulation[J].Parallel Comput.,2001,27:1611-1639.
[100]雷德明，严新平.多目标智能优化算法及其应用[M].科学出版社，2009
[101]焦李成，尚荣华，马文萍.多目标优化免疫算法理论和应用[M].科学出版社，2010
[102]翁妙凤.基于并行进化规划的Job Shop动态调度策略[J].小型微型计算机系统,2000,21(6)
[103] Lova A, Maroto C, Tormosp. A multicriteria heuristic method to improve resourceallocation in multiproject scheduling[J]. European Journal of Operational Research,2000,127(2),408-424.
[104]孟鑫,范钦杰,王宏仁.集值离散动力系统的拓扑遍历性与链遍历性[J].2011,29(1):20-22.
[105]杨润生.拓扑遍历映射[J].数学学报,2001,44(6):1063-1068.
[106] KOLISCH R. Seri al and parallel resource constrained project scheduling methodsrevisited: theory and computation[J]. European Journal of Operational Research,1996,90(2):320-333.
[107] MIAO Z, YASSINE A. Task scheduling of parallel development projects using geneticalgorithms[C]//Proceedings of2004American Society of Mechanical EngineersDesign Automation Conference. Chicago, Ill., USA: ASME,2004:1-11.
[108]陈根方,张立印.基于串行计算的排序算法实证分析[J].杭州师范大学学报,2010,9(2),135-138.
[109]平萍.元胞自动机原理及其在密码学的应用研究[D].南京理工大学,2009.
[110]郑大钟,赵千川.离散事件动态系统[M].清华大学出版社,2000,25-29.
[111]孔宪娟,高自友,李克平.考虑近邻和次近邻车辆影响的单车道元胞自动机模型[J].北京交通大学学报,2006,30(6):11-15.
[112]陈健,雷洪,田帅,赫冀成.基于排队论的元胞自动机在港口交通流的研究[J].沈阳师范大学学报（自然科学版）,2009,27(3):319-322.
[113]李松，张杰，贺国光.基于元胞自动机模型的交通流混沌仿真研究[J].计算机工程与应用,2007(32):20-22.
[114]张晋,王慧,李平.基于一维元胞自动机的可变时距跟车模型[J].浙江大学学报,2006,40(2):233-237.
[115] EMMERICH H, RANK E. An improved cellular automaton model for traffic flowsimulation[J]. Physica A,1997,234(3):676-686.
[116]吕晓阳,孔令江,刘慕仁.一维元胞自动机随机交通流模型的宏观方程分析[J].物理学报,2001,50(7):1255-1259.
[117] Kareem, E.I.A., Jantan, A.,"An approach for monitoring pedestrian by usingmulti-connect architected MCA associative memory", International Journal of DigitalContent Technology and its Applications, vol.3, no.2, pp.75-82,2009.
[118]董力耘,薛郁,戴世强.基于跟车思想的一维元胞自动机交通流模型[J].应用数学与力学,2002,23(4):331-337.
[119]张杰,贺国光.基于一维元胞自动机模型的交通流混沌研究[J].武汉理工大学学报（交通科学与工程版）,2009,33(1):33-36.
[120] Jianping Chen, Jiawei Ye. Research of Cellular Automata Model of Dockyard ShipShifting Scheduling Problem and Its Ant Colony AlgorithmJ]. International Journal ofAdvancements in Computing Technology,2012,4(1):312-319.
[121]马良，朱刚，宁爱兵.蚁群优化算法[M].科学出版社，2008
[122] FIEMS D, MAERTENS T, BRUNEEL H. Qeueueing systems with different types ofserver interruptions[J] European Journal of Operational Research,2008,188(3):838-845.
[123]沈艳.神经网络理论研究及在舰船运动预报中的应用[D].哈尔滨工程大学,2005.
[124]周开利，康耀红.神经网络模型及其MATLAB仿真程序设计[M].清华大学出版社，2005
[125] Fieldsend J E, Singh S. Pareto evolutionary neural netwoerks[J]. IEEE Transactions onNeural Networks,2005,16(2),338-354.
[126]马铭,周春光,张利彪.一种优化模糊神经网络的多目标微粒群算法[J].计算机研究与发展,2006,43(12),2104-2109.
[127] Castillo P A. Optimization de perceptrones multicapa mediante algoritmos evolutivos[J].Ph. D. Dissertation, Univ. Granada, Spain,2000.
[128]王岩,周春光,黄艳新.基于最小不确定神经网络的茶味觉信号识别[J].计算机研究与发展,2005,42(1),66-71.
[129] Deigado M, Pegalajar M C. A mult-objective genetic algorithm for obtaining theoptimal size of a recurrent neural network for grammatical inference[J]. PatternRecognition,2005,38,1444-1456.
[130]郎茂祥.配送车辆优化调度模型与算法[M].电子工业出版社，2009,146-151.
[131]徐杰,黄德先.基于混合粒子群算法的多目标车辆路径研究[J].计算机集成制造系统,2007,13(3),573-579.
[132]肖健梅,李军军,王锡准.求解车辆路径问题的改进粒子群优化算法[J].计算机集成制造系统,2005,11(4),577-581.
[133] Christian P. A simple and effective evolutionary algorithm for the vechicle routingproblem[J]. Computers and Operation Research,2004,31(12),1985-2002.
[134]郎茂祥.多配送中心车辆调度问题的模型与算法研究[J].交通运输系统工程与信息,2006,6(5),
[135]张炯,郎茂祥.有时间窗配送车辆调度问题的禁忌搜索算法[J].北方交通大学学报,2004,28(2),
[136]谢秉磊,郭耀煌,郭强.动态车辆路径问题:现状与展望[J].系统工程理论方法应用,2002,11(2),
[137]肖增军,李军.动态网络车辆路径问题[J].系统工程,2004,22(7),
[138] Jianping Chen, Jiawei Ye. Modeling of Shipyard Ship Shifting Scheduling Problem andits Five-point Approach Method based on Dynamic Programming[J]. InternationalJournal of Digital Content Technology and its Applications,2011,5(10):211-218.
[139]陈建平.船舶修理任务和资源合理配置的线性规划分析[J].船海工程,2010,39(195):41-43.
[140]周洋.国内修船单船管理模式发展概述[J].中国修船,2008,21(6),5-8.
[141]陈建平.船舶修理生产资源的合理分配[J].中国修船,2009,22(135):6-8.
[142]王庚，王敏生.现代数学建模方法[M].科学出版社，2008
[143] Steven J.Leon. Linear Algebra with Applications[M].机械工业出版社，2009
[144]陈建平.船舶常规修理和改装的经济性分析[J].江苏船舶，2008,25(6):25-27.
[145]任玉杰.数值分析及其MATLAB实现[M].高等教育出版社，2007