船台吊装过程调度优化关键技术研究
|
摘要
大型船舶船体装配过程是船舶制造中的重要环节,它不同于作业车间和流水车间的制造过程,具有设计制造并行、小批量、高度依赖船台资源、制造过程受多种外界干扰等不同于传统制造系统的特殊性和复杂性,并且船舶的生产完全是面向订单的生产模式而不同于面向库存的生产模式。在这种形势下,大型船舶装配过程处于复杂而又具有变化的环境中,其调度控制过程也变得复杂和特殊,建立在传统的生产运作管理理论与运筹学基础上的生产调度与控制方法,已经不能满足其调度控制要求。本文的研究针对这一问题,从建模、调度技术和仿真等方面对大型船舶船体吊装过程的调度控制进行研究。
主要包括下述主要工作:
(1)为了实现对船台吊装过程的规范化描述、准确表示吊装过程,提出了基于共享合成着色时间Petri网(Colored Timed Petri Net, CTPN)的船台吊装过程建模方法。在建立吊装过程典型操作组件模型的基础上,根据共享合成原理构建出吊装过程的模型,所建CTPN模型能够包含吊装中的技术约束、资源约束和时间等信息,支持对船舶吊装过程调度和控制策略的仿真分析。该建模方法给出了吊装过程的便于计算机表示和操作的数学描述形式。
针对所建船舶吊装过程CTPN模型,给出了一种利用极大加法代数求解模型中分段吊装时间的算法。对所建模型的性能分析方法进行了说明,讨论了结合简化规则和可达图方法的定性分析策略。
(2)为了在既有的吊装设备、场地等资源条件约束下,寻找可能的最短船台周期并且尽量提前某些重要时间节点,提出了一种基于改进遗传算法的调度算法。根据吊装过程调度问题的特点,对传统遗传算法进行改进,给出了一种适合这类问题的交叉算子,通过避免不可行子个体的产生从而大大提高了搜索的效率和解的质量。某74,500DWT散货船吊装过程调度优化实例说明这种遗传算法对于解决时间安排、设备使用分配等吊装过程调度问题是有效的。为了探讨这类整数编码遗传算法的机理,获得改进和分析此类遗传算法的理论基础,采用Markov链对基于整数编码的遗传算法进行了收敛性分析。提出了一种整数编码遗传算法种群多样性量化定义方法,求解出了特定“基因对”顺序变换概率的公式表示,说明了遗传算法收敛性与遗传算法参数间的关系。
(3)降低分段堆放成本是船舶企业的重要课题,从吊装过程调度安排的角度出发提出了一种以分段堆放成本最小为目标的拉格朗日松弛算法。将吊装过程调度问题归结为一种新的包含任务先序约束、机器能力约束以及某些任务同时占用多台机器情况的平行机排序问题。采用拉格朗日算法对船体装配过程中的先序约束、起重设备能力约束进行松弛,然后采用次梯度法求解拉格朗日对偶问题,再通过一定的启发式规则获得可行解。实际集装箱船例子和仿真数据的计算结果表明,这种方法能够满足实际工程生产的求解速度需要和精度要求。
(4)吊装过程受多种因素影响,为了提高吊装生产效率和稳定性,必须更加准确地确定吊装时间,根据干扰的影响及时进行有效的重调度。在对分段大小、重量、船型、分段所在区域以及建造季节等吊装时间影响因素分析的基础上,提出了利用模糊神经网络(FNN)确定吊装所需时间的方法。提出了船舶吊装过程重调度策略选择评价指标,给出了基于分支定界思想的重调度优化算法,并结合船舶吊装生产实例,对以上重调度控制技术进行了验证。
(5)在仿真和工程实例方面,提出了基于高层体系架构(HLA)的协同仿真平台,对其逻辑结构、功能模块划分、数据以及时间管理等关键技术进行了分析和定义;初步建立了协同仿真平台的各功能模块。建立了大型船舶装配过程仿真模型和调度控制策略之间的桥梁,实现了仿真环境下的调度策略性能分析。在吊装过程三维动态仿真模块中,提出了一种能够增强虚拟吊装过程沉浸感的查看路径规划方法。根据样条小波多分辨率表示曲线原理,并结合查看通道的宽度情况生成查看路径。给出了路径规划的具体实现步骤并展示了在吊装过程三维虚拟场景中进行查看的例子。
Erection operation scheduling on dock (EOS), differing from both job-shop and flow-shop, is a kind of new type one, whose special and complex characteristics include large scale, existing occupying multiple machines simultaneously, unbalance load of machine. And its production mode is order oriented style. Under the complex and change chaos circumstance, the control system of EOS is nonlinear. The production scheduling and control methods, based on traditional production operation theory, can’t meet the need of the control of EOS. In order to solve this problem, supported by the Key Item Program of S&T Committee, Shanghai City, China project‘Research on material flow in shipbuilding enterprise’(Grant No.04DZ11004), the thesis’topic is research on the intelligent coordinated scheduling technologies of EOS and its key technologies. The paper discussed from different points, and expected gain breakthrough in theory, method, supporting technologies and engineering application. The following are the main works in this paper:
(1) Block Erection in Shipyard is a complex process. A method based on sharing synthesis of timed colored Petri net for modeling of block erection process in shipyard is proposed. A min-max algebra algorithm is used to calculate the time. The information of erection constraints, facilities, space and time consuming are involved and expressed clearly in this model. The approach can be employed to model the erection process of single vessel or multi-vessel. Finally, a case study of the modeling and optimization of erection process of two 74,500 DWT bulk carriers is given and the result shows that this modeling method is effective.
(2) A method based on modified genetic algorithm for optimization of block erection process is proposed. Firstly, the constraints of erection network and the production ability of block are analyzed and the objective function to minimize the makespan on dock is given. Then, a modified genetic algorithm is used to optimize this problem, and the coding approach and a crossover operator are introduced in details. Finally, a case study of the optimization of erection process of a 74500 DWT bulk carrier is given and the result shows that this optimization method is effective. The convergence rate of modified genetic algorithm with integer coding was analyzed. Firstly, a diversity of population of genetic algorithm is defined which can formally express the degree of premature for this kind of genetic algorithm. Then the formalized equation is given about the relationship between diversity and crossover rate, mutation rate and population size. Finally, a simulation experiment about block assembly sequence scheduling in a shipyard is introduced to verify the above opinions.
(3) A new Lagrangian Relaxation approach is developed to schedule the erection operations to minimize the inventory cost of blocks. It corresponds to a parallel machine problem with the precedence constraints of tasks, the capacity constraints of machines and the situation that some tasks must occupy multiple machines simultaneously. The objective is to minimize the weighted total completion time. A mathematical formulation of the problem is presented firstly. Then the task precedence constraints rather than crane machine capacity constraints are relaxed by Lagrangian relaxation method and the subgradient method is used to solve the Lagrangian dual problem. Finally the results are modified by some heuristic operation. A practical example of container vessel that includes 65 blocks is solved by the proposed approach, and the results show that the Lagrangian relaxation method is able to solve industrial-dimensioned problems within reasonable time and accuracy.
(4) The disturbance types of erection time were analyzed and a rescheduling method was proposed. A novel fuzzy neural network based algorithm for analyzing disturbance of erection time was proposed. The learning samples for FNN were setup by simulation evaluation. The relation between the input parameters of block and erection time was built by FNN. The branch and bound algorithm is used to rescheduling the erection operations. The construction of feasible tree and the branch fathom rules were given. The nodes-including rule and lower-bound rule were used to cut the invalid branches.
(5) A simulation platform based on HLA was proposed. This platform can help user to simulation their scheduling results dynamically. The architecture and function modules of this platform were analyzed and defined. The data and time management methods were discussed and the prototype of this platform was developed. In 3D simulation module, a method about navigation path multi-resolution planning based on wavelets was proposed to eliminate the vision dither phenomenon and enhance the immerse feeling in virtual environment of shipbuilding. The theory and realization of constructing navigation path were introduced in details and an example about block erection is given.
主要包括下述主要工作:
(1)为了实现对船台吊装过程的规范化描述、准确表示吊装过程,提出了基于共享合成着色时间Petri网(Colored Timed Petri Net, CTPN)的船台吊装过程建模方法。在建立吊装过程典型操作组件模型的基础上,根据共享合成原理构建出吊装过程的模型,所建CTPN模型能够包含吊装中的技术约束、资源约束和时间等信息,支持对船舶吊装过程调度和控制策略的仿真分析。该建模方法给出了吊装过程的便于计算机表示和操作的数学描述形式。
针对所建船舶吊装过程CTPN模型,给出了一种利用极大加法代数求解模型中分段吊装时间的算法。对所建模型的性能分析方法进行了说明,讨论了结合简化规则和可达图方法的定性分析策略。
(2)为了在既有的吊装设备、场地等资源条件约束下,寻找可能的最短船台周期并且尽量提前某些重要时间节点,提出了一种基于改进遗传算法的调度算法。根据吊装过程调度问题的特点,对传统遗传算法进行改进,给出了一种适合这类问题的交叉算子,通过避免不可行子个体的产生从而大大提高了搜索的效率和解的质量。某74,500DWT散货船吊装过程调度优化实例说明这种遗传算法对于解决时间安排、设备使用分配等吊装过程调度问题是有效的。为了探讨这类整数编码遗传算法的机理,获得改进和分析此类遗传算法的理论基础,采用Markov链对基于整数编码的遗传算法进行了收敛性分析。提出了一种整数编码遗传算法种群多样性量化定义方法,求解出了特定“基因对”顺序变换概率的公式表示,说明了遗传算法收敛性与遗传算法参数间的关系。
(3)降低分段堆放成本是船舶企业的重要课题,从吊装过程调度安排的角度出发提出了一种以分段堆放成本最小为目标的拉格朗日松弛算法。将吊装过程调度问题归结为一种新的包含任务先序约束、机器能力约束以及某些任务同时占用多台机器情况的平行机排序问题。采用拉格朗日算法对船体装配过程中的先序约束、起重设备能力约束进行松弛,然后采用次梯度法求解拉格朗日对偶问题,再通过一定的启发式规则获得可行解。实际集装箱船例子和仿真数据的计算结果表明,这种方法能够满足实际工程生产的求解速度需要和精度要求。
(4)吊装过程受多种因素影响,为了提高吊装生产效率和稳定性,必须更加准确地确定吊装时间,根据干扰的影响及时进行有效的重调度。在对分段大小、重量、船型、分段所在区域以及建造季节等吊装时间影响因素分析的基础上,提出了利用模糊神经网络(FNN)确定吊装所需时间的方法。提出了船舶吊装过程重调度策略选择评价指标,给出了基于分支定界思想的重调度优化算法,并结合船舶吊装生产实例,对以上重调度控制技术进行了验证。
(5)在仿真和工程实例方面,提出了基于高层体系架构(HLA)的协同仿真平台,对其逻辑结构、功能模块划分、数据以及时间管理等关键技术进行了分析和定义;初步建立了协同仿真平台的各功能模块。建立了大型船舶装配过程仿真模型和调度控制策略之间的桥梁,实现了仿真环境下的调度策略性能分析。在吊装过程三维动态仿真模块中,提出了一种能够增强虚拟吊装过程沉浸感的查看路径规划方法。根据样条小波多分辨率表示曲线原理,并结合查看通道的宽度情况生成查看路径。给出了路径规划的具体实现步骤并展示了在吊装过程三维虚拟场景中进行查看的例子。
Erection operation scheduling on dock (EOS), differing from both job-shop and flow-shop, is a kind of new type one, whose special and complex characteristics include large scale, existing occupying multiple machines simultaneously, unbalance load of machine. And its production mode is order oriented style. Under the complex and change chaos circumstance, the control system of EOS is nonlinear. The production scheduling and control methods, based on traditional production operation theory, can’t meet the need of the control of EOS. In order to solve this problem, supported by the Key Item Program of S&T Committee, Shanghai City, China project‘Research on material flow in shipbuilding enterprise’(Grant No.04DZ11004), the thesis’topic is research on the intelligent coordinated scheduling technologies of EOS and its key technologies. The paper discussed from different points, and expected gain breakthrough in theory, method, supporting technologies and engineering application. The following are the main works in this paper:
(1) Block Erection in Shipyard is a complex process. A method based on sharing synthesis of timed colored Petri net for modeling of block erection process in shipyard is proposed. A min-max algebra algorithm is used to calculate the time. The information of erection constraints, facilities, space and time consuming are involved and expressed clearly in this model. The approach can be employed to model the erection process of single vessel or multi-vessel. Finally, a case study of the modeling and optimization of erection process of two 74,500 DWT bulk carriers is given and the result shows that this modeling method is effective.
(2) A method based on modified genetic algorithm for optimization of block erection process is proposed. Firstly, the constraints of erection network and the production ability of block are analyzed and the objective function to minimize the makespan on dock is given. Then, a modified genetic algorithm is used to optimize this problem, and the coding approach and a crossover operator are introduced in details. Finally, a case study of the optimization of erection process of a 74500 DWT bulk carrier is given and the result shows that this optimization method is effective. The convergence rate of modified genetic algorithm with integer coding was analyzed. Firstly, a diversity of population of genetic algorithm is defined which can formally express the degree of premature for this kind of genetic algorithm. Then the formalized equation is given about the relationship between diversity and crossover rate, mutation rate and population size. Finally, a simulation experiment about block assembly sequence scheduling in a shipyard is introduced to verify the above opinions.
(3) A new Lagrangian Relaxation approach is developed to schedule the erection operations to minimize the inventory cost of blocks. It corresponds to a parallel machine problem with the precedence constraints of tasks, the capacity constraints of machines and the situation that some tasks must occupy multiple machines simultaneously. The objective is to minimize the weighted total completion time. A mathematical formulation of the problem is presented firstly. Then the task precedence constraints rather than crane machine capacity constraints are relaxed by Lagrangian relaxation method and the subgradient method is used to solve the Lagrangian dual problem. Finally the results are modified by some heuristic operation. A practical example of container vessel that includes 65 blocks is solved by the proposed approach, and the results show that the Lagrangian relaxation method is able to solve industrial-dimensioned problems within reasonable time and accuracy.
(4) The disturbance types of erection time were analyzed and a rescheduling method was proposed. A novel fuzzy neural network based algorithm for analyzing disturbance of erection time was proposed. The learning samples for FNN were setup by simulation evaluation. The relation between the input parameters of block and erection time was built by FNN. The branch and bound algorithm is used to rescheduling the erection operations. The construction of feasible tree and the branch fathom rules were given. The nodes-including rule and lower-bound rule were used to cut the invalid branches.
(5) A simulation platform based on HLA was proposed. This platform can help user to simulation their scheduling results dynamically. The architecture and function modules of this platform were analyzed and defined. The data and time management methods were discussed and the prototype of this platform was developed. In 3D simulation module, a method about navigation path multi-resolution planning based on wavelets was proposed to eliminate the vision dither phenomenon and enhance the immerse feeling in virtual environment of shipbuilding. The theory and realization of constructing navigation path were introduced in details and an example about block erection is given.
引文
[1] Andritsos F, Perez-Prat J. The Automation and Integration of Production Processes in Shipbuilding[M]. European Commission Joint Research Centre, 2000.
[2]李堃，现代造船工程[M].哈尔滨:哈尔滨工程大学出版社,1998.
[3]应长春,徐学光.论现代造船体制的特征和实践,中国造船,1995,(2),1-11.
[4] Kim Hongtae, Applying digital manufacturing technology to ship production and the maritime environment [J]. Integrated Manufacturing Systems. 2002, 13(5): 295-305.
[5] Lee, J.K., Lee, K.J., Hong, J.S., etc., DAS: Intelligent scheduling systems for shipbuilding [J]. AI Magazine. 1995, 16(4): 78-94.
[6] Kim H.T., Lee J.G., Lee S.S. A simulation based shipbuilding system for evaluation of validity in design and manufacturing[C]. Proceedings of IEEE International Conference on Systems, Man and Cybernetics. Washington, D.C., USA: IEEE, 2003: 522-529.
[7] Tu YiLiu. Automatic scheduling and control of a ship web welding assembly line [J]. Computer in Industry. 1996, 29(1996): 169-177.
[8] Charles McLean, Shao G. Simulation of shipbuilding operations [C]. Proceedings of the 2001 Winter Simulation Conference. Washington, D.C., USA: IEEE, 2001: 870-876.
[9] Lee, J.K, Choi, H.R., Yang, O.R., etc. Scheduling shipbuilding using a constraint directed graph search: DAS-ERECT[J]. Intelligent Systems in Accounting, Finance, and Management, 1994, 3:111-125.
[10] Park K,Lee K, Park S, et al. Modeling and solving the spatial block scheduling problem in a shipbuilding company. Computers Industry Engineering, 1996, 30(3):357-364.
[11]刘建峰.上海交通大学博士学位论文:基于PWBS船体建造运作策略研究[D].上海:上海交通大学,2000.
[12]陈强.哈尔滨工程大学博士学位论文:中心制造船模式的研究与应用[D].哈尔滨:哈尔滨工程大学,2002.
[13] Iwase, N. Rapid growth of BRICs boosts shipbuilding demand [J]. Steel Times International. 2006, 30(1): 16-16.
[14]王言英,韩国的造船厂及其建造能力简析[J].船舶. 1996, 3:56-58.
[15] Cho Kyu-Kab, Sun Jung-Guy, Oh Jung-Soo, An automated welding operation planning system for block assembly in shipbuilding, International Journal of Production Economics, 1999,60-61: 203-209.
[16] Thomas Lamb(FL), Hyun Chung(St), etc. Simulation based Performance Improvement for Shipbuilding Processes. __
[17] Lee J.K., Lee K.J., Park H.K, et al. Developing scheduling systems for Daewoo shipbuilding: DAS project [J]. European Journal of Operational Research, 1997, 97(2):380～395.
[18] Jong Gye Shin,etc.,A modeling and simulation of production process in subassembly lines at a shipyard,2001.__
[19] Aoyama, K., et al. System of Parts design and Manufacturing in Shipbuilding [J]. JSNAJ, 178: 551-561.
[20]袁崇义,Petri网原理与应用[M],北京:电子工业出版社, 2005.
[21] Srinivasan R. S., Modeling and performance analysis of cluster tools using Petri nets [J]. IEEE Transactions on Semiconductor Manufacturing, 1998(11):394–403.
[22] Lin S.Y. and Huang H.P. Modeling and emulation of a furnace in IC fab based on colored-timed Petri net[J], IEEE Transactions on Semiconductor Manufacturing. 1998(11):410–420.
[23] Wen H., Fu L.C., Huang S.S. Modeling, Scheduling, and Prediction in Wafer Fabrication Systems Using Queuing Petri Net and Genetic Algorithm[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Soul, Korea, 2001:3559-3564.
[24] Chung H., Lamb T. A generic shipyard computer model- a tool for design for production [J]. Journal of Ship Production, 2000, 16(3): 160-172.
[25] Alfeld L.E., Pilliod C.S., Wilkins J. R., A Simulation Model of the Shipbuilding Process[J]. Journal of Ship Production, 14(1):33-40.
[26] IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) [A].Object Model Template (OMT) Specification, 2000.
[27]苟凌怡,熊光楞,杨流辉.支持虚拟样机的协同仿真平台关键技术研究[J],系统仿真学报, 2002,14(3): 348-355.
[28]吴哲辉, Petri网导论[M],北京:机械工业出版社, 2006.
[29] Rodrigues, Cassio L., Guerrero, Dalton D.S., etc., Model checking in object-oriented Petri nets[C], Conference Proceedings-IEEE International Conference on Systems, Man and Cybernetics, SMC, 2004, 5: 4977-4982.
[30] Boleslaw M., Charles S., Integrating object-oriented design with Petri nets - Case study of ATM system[C], Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2003, 2: 1499-1505.
[31] Lawler, E.L. Sequencing jobs to minimize total weighted completion time subject to precedence constraints [J]. Annals of Discrete Mathematics. 1978, 2: 75-90.
[32] Lenstra, J.K., Rinnooy, K.A.H. Complexity of scheduling under precedence constraints [J]. Operations Research. 1978, 100(1): 22-36.
[33] Horn, W.A. Single-machine job sequencing with treelike precedence ordering and linear delay penalties [J]. SIAM Journal on Applied Mathematics. 1972, 23: 189-202.
[34] Du, J., Leung, J.Y.T and Young, G.H. Scheduling chain structured tasks to minimize makespan and mean flow time [J]. Information and Computation. 1991, 92(2): 219-236.
[35] Han, J., Wen, J. and Zhang, G. A new approximation algorithm for UET-scheduling with chain-type precedence constraints [J]. Computers Operation Research. 1998, 25(9): 767-771.
[36] Moukrim, A. Optimal scheduling on parallel machines for a new order class [J]. Operations Research Letters. 1999, 24(1/2): 91-95.
[37] Chekuri, C., Motwani, R., Natarajan, B., etc. Approximation techniques for average completion time scheduling[C]. Proceedings of the Eighth ACM-SIAM Symposium of Discrete Algorithms. 1997: 609-618.
[38] Ramachandra, G. and Elmaghraby, S. E. Sequencing precedence-related jobs on two machines to minimize the weighted completion time [J]. International Journal of Production Economics. 2006, 100:44-58.
[39] Graham R.L. Bounds for certain multiprocessing anomalies [J]. Bell System Technical Journal.1966, 45: 1563-1581.
[40] Dell’Amico, M. and Martello, S. Optimal scheduling of tasks on identical parallel processors [J]. ORSA Journal on Computing. 1995, 7(2): 191-200.
[41] Nessah, R., Yalaoui, F. and Chu C., A Branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates [J]. Computers & Operations Research. doi: 10.1016/j.cor.2006.07.010.
[42] Franklina, M.B.T. and Vinicius, A.A. A Lagrangian-based heuristic for the capacitated lot-sizing problem in parallel machines [J]. European Journal of Operational Research. 2006, 175(2): 1070-1083.
[43] Ventura, J.A. and Kim, D. Parallel machine scheduling with earliness-tardiness penalties and additional resource constraints [J]. Computers and Operations Research. 2003, 30:1945-1958.
[44] Hou E.S.H., Ansari N. and Ren H. A genetic algorithm for multiprocessor scheduling [J]. IEEE Transactions on Parallel and Distributed Systems. 1994, 5(2):113-120.
[45] Yamauchi M., Watanabe T. A heuristic algorithm FSD for the legal firing sequence problem of Petri nets [A]. In Proc. of IEEE International Conference on Systems, Man and Cybernetics [C]. California, USA: 1998, 78-83.
[46] Matsumoto, Tadashi. Finding legal firing sequences of Petri nets by means of dynamic programming included linear programming[C]. Proceedings of the IEEE Conference on Decision and Control. 1996, 4459-4466.
[47] Matsumoto, Tadashi. Finding legal firing sequences in submarking reachability problems of Petri nets by discrete-time Pontryagin's minimum principle; Proceedings - IEEE International Symposium on Circuits and Systems, v2, Circuit Theory and Power Systems Communication and Multimedia. 1997, 1017-1020.
[48] Takahashi K., Ona L. and Satoh H., etc. An efficient genetic algorithm for reachability problems [A]. In Proceeding of 14th Hawaii International Conference on System Sciences [C], Hawaii, USA: 1997, 5(7-10) :89-98.
[49]李勇,曹广益,朱新坚,一种基于单亲遗传算法的Petri网发射路径求解算法[J].系统仿真学报. 2005, 17(1): 203-206.
[50] Yasuhisa Okumoto. Optimization of block erection using a genetic algorithm[J]. Journal of Ship Production, 2002, 18(2):116-119.
[51] Scott J.A., Todd A.S. and Sen P.,An Evolution Approach to the Scheduling of Ship Design and Production Process, 1998 Elsevier BY, :351-357.
[52] Neumann R.J. Network Scheduling Development in an MRP II Environment[J]. Journal of Ship Production, 1994, 10(4).
[53] Manivannan S., Banks, J. Real-time control of a manufacturing cell using knowledge-based simulation[C]. Proceedings of the Winter Simulation Conference, IEEE, Pos. NJ, 1991: 251-260.
[54] Bongaerts L., Monostori L., McFarlane D., etc. Hierarchy in distributed shop floor control [J]. Computers in Industry, 2000, 43: 123~137
[55] Callegari M., Palpacelli M.C., Principi M., Dynamics modelling and control of the 3-RCC translational platform[J], Mechatronics, 2006, doi:10.1016/j.mechatronics.2006.06.001.
[56] Son Changman, Optimal control planning strategies with fuzzy entropy and sensor fusion forrobotic part assembly tasks [J], International Journal of Machine Tools & Manufacture, 2002, 42: 1335–1344.
[57] Bai S.X., Gershwin S.B. Scheduling manufacturing system with work-in-process inventory control: single-part-type systems[J]. IIE Transaction. 1995, 27:599-617.
[58] Amemiya, T., Production Planning System for Oppama Shipyard. ICCAS'94, 2:12,3-14.
[59] Nakayama J., Expert Process Planning System of CIM for Shipbuilding. ICCAS'94, 2:12, :55-66.
[60]陈强,船舶制造系统的复杂性及其控制[J],中国造船,2002年第1期.
[61] Liao D., Chang S. and Pei K., etc., Daily Scheduling for R&D Semiconductor Fabrication [J]. IEEE Transactions on Semiconductor Manufacturing. 1996, 9(4):550-561
[62] Vieira G.E., Jeffrey W.H. and Edward L., Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies [J]. International Journal of Production Research, 2000, 38(8): 1899-1915.
[63] Church L.K., Uzsoy R., Analysis of periodic and event driven rescheduling policies in dynamic shops, International Journal of Computer Integrate Manufacture. 5(1992):153-163.
[64] Vieira G.E., Herrmann J.W. and Lin E., Rescheduling manufacturing systems: a framework of strategies, policies, and methods, Journal of Scheduling. 2003, 6:39–62.
[65] Kanet D., Moon I., Rescheduling algorithms in case of unit failure for batch process management [J]. Computers and Chemical Engineering, 1997, 21(S):1067-1072.
[66] Hall N.G., Potts C.N., Rescheduling for new orders, Operational Research, 2004, 52:440–453.
[67] Vallapuzha S., Meter E.C.D., Choudhuri S., etc., An investigation into the use of spatial coordinates for the genetic algorithm based solution of the fixture layout optimization problem [J]. International Journal of Machine Tools & Manufacture, 2002, 42:265–275.
[68] K. H. Lee, Intelligent Approach to a CAD system for the layout design of a ship engine room[J]. Computers industry engineering. 34(3): 599-608.
[69]张志英,江志斌,基于规则推理的自动船体装配工艺生成方法[J].上海交通大学学报. 2007, 41(1):31-35.
[70]袁崇义,Petri网原理[M],北京:电子工业出版社, 1998.
[71] Reid D.J., Constructing Petri net models using genetic search [J], Mathl. Comput. Modelling, 1998, 27(8):53-103.
[72] Reyes A., Yu H., Kelleher G. etc., Integrating Petri nets and hybrid heuristic search for the scheduling of FMS [J]. Computers in Industry, 2002, 47(2002):123-138.
[73] Lee Y.K., Park S.J., OPnets: An Object-Oriented high-level Petri net model for real-time system modeling [J], Journal of System Software, 1993, (20): 69-86
[74] Chen K.Y., A Petri-net and entity-relationship diagram based object-oriented design method for manufacturing systems control [J]. International Journal of Computer Integrated Manufacturing. 1997, 10(1-4): 17-28
[75]续爱民,金烨.基于共享合成时间Petri网的船舶吊装过程建模与仿真[J].计算机集成制造系统,2007,13(3):465-471.
[76]田锋,李人厚,张金成.基于赋时着色Petri网的协同工作建模和性能研究[J].西安交通大学学报, 2003, 37(6): 560-564.
[77]窦万春,蔡士杰.基于Petri网的工作流技术复合建模与系统分解[J].计算机集成制造系统-CIMS, 2002,8(5): 361-365.
[78] Jeng M.D., DiCesare F., A synthesis method for Petri net modeling of automated manufacturing systems with shared resources [C], Proceedings of the 31st Conference on Decision and Control, Tucson, Arizona. December 1992, 1184-1189.
[79] Ferrarini L., Trioni M., Modeling shared resources with generalized synchronization within a Petri net bottom-up approach[J], IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1996, 26(4):653-659.
[80]蒋昌骏. Petri网的行为理论及其应用[M].北京:高等教育出版社,2003.
[81]杨欣,陈丽娟,费树岷,基于柔性制造系统的Petri网模型简化规则[J].计算机集成制造系统. 2006, 12(11):1789-1793.
[82] Starke S. Reachable analysis of Petri nets using symmetries[J]. System Analysis Model Simulation. 1991, 14 (8): 293-303.
[83] Patrice B., Gerard B., Pascal A, etc. Verification technique for time Petri nets[A], IEEE International Conference on Systems, Man and Cybernetics[C]. United States: Institute of Electrical and Electronics Engineers Inc., 2004.4278-4283.
[84]姜浩,董逸生,一种基于扩展时间Petri网的工作流时间性能评价方法[J].计算机研究与发展. 2005, 42(5): 849-855.
[85] Xu Ai-min, Jin Ye., A Genetic Algorithm for Erection Scheduling at a Dock[J], Journal of Ship Production, 2007,23(1):17-22.
[86] Murata T., Ishibuchi H. and Tanaka H., Multi-objective genetic algorithm and its application to flow-shop scheduling [J]. Computers and Industrial Engineering, 1996, 30(4):957-968.
[87] Kailyanmoy Deb., Muti-Objective Optimization Using Evolutionary Algorithms [M], John Wiley & Sons Ltd, 2001.
[88] Davis L., Handbook of Genetic Algorithms [M]. New York: Van Nostrand Reinhold, 1991.
[89] Poon P.W., Carter J.N. Genetic algorithm crossover operators for ordering applications[J]. Computer Operation Resarch, 1995, 22(1):135-147.
[90] Leung Y., Gao Y., Xu Z.B. Degree of population diversity-a perspective on premature convergence in genetic algorithms and its Markov-chain analysis[J]. IEEE Trans Neural Network, 1997, 8(5): 1165-1176.
[91]王凌.车间调度及其遗传算法[M],北京:清华大学出版社,2003.
[92] Jakub W., Theoretical foundations of order-based genetic algorithms[J], Fundamenta Informaticae, 1996, 28 (3/4): 423-430.
[93] Goldberg D.E., GA in Search, Optimisation, and Machine Learning[M]. Addison-Wesley. , 1989.
[94] Rudolph G., Convergence analysis of canonical genetic algorithms[J],IEEE Transaction on neural networks, 1994, 5(1):96-101.
[95] Suzuki J.,A Markov Chain Analysis on Simple Genetic Algorithms[J],IEEE Transaction on systems, man, and cybernetics, 1995, 25(4).
[96] Leung Y., Gao Y., and Xu Z. B.,Degree of Population Diversity—A Perspective on Premature Convergence in Genetic Algorithms and its Markov Chain Analysis[J],IEEE Transactions on neural networks, 1997,8(5):1165-1176.
[97] Liu J., Cai Z X, Liu J Q, Premature Convergence in Genetic Algorithm: Analyss and PreventionBased on Chaos Operation[C], In: The Third World Congress on Intelligent Control and Automation, Hefei, China, 2000,6:495-499.
[98]王霞,周国标,遗传算法收敛率的下鞅分析[J],应用数学, 2003,16(4):130-135.
[99] Murata T., Genetic algorithms for flow shop scheduling problems[J], Computers Industry Engineering, 1996, 30(4):1061-1071.
[100] Reeves, Colin R,A Genetic algorithm for flow shop sequencing[J],Computers & Operations Research, 1995, 22(1):5-13.
[101] Tang L., Xuan H. and Liu J., A new Lagrangian relaxation algorithm for hybrid flow-shop scheduling to minimize total weighted completion time[J], Computers & Operations Research, 2006, 33: 3344-3359.
[102] Toledo F.M.B., Armentano V.A., A Lagrangian-based heuristic for the capacitated lot-sizing problem in parallel machines[J], European Journal of Operational Research, 2006, 175:1070-1083.
[103] Diaby, M., Bahl, H.C., Karwan, M.H.,etc., A Lagrangian relaxation approach for very-large-scale capacitated lot sizing. Management Science, 1992, 38:1329-1340.
[104]邢文训,谢金星,现代优化计算方法(第2版)[M],北京,清华大学出版社,2005,210-244.
[105] Luh P.B., Yu D. and Soorapanth S., A Lagrangian relaxation based approach to schedule asset overhaul and repair services[J], IEEE Transactions on Automation Science and Engineering, 2005, 2(2):145-157.
[106] Baptistea P., Flaminib M. and Sourdc F., Lagrangian bounds for just-in-time job-shop scheduling[J], Computers & Operations Research, doi:10.1016/j.cor.2006.05.009 .
[107] Chen H., Luh P.B., An alternative framework to Lagrangian relaxation approach for job shop scheduling[J]. European Journal of Operational Research. 2003, 149(2003): 499-512.
[108] Fisher M.L., The Lagrangian relaxation method for solving integer programming problems[J], Management Science, 1981, 27(1):1-18.
[109] Zhao X., Luh P. B., Wang J., Surrogate gradient algorithm for Lagrangian relaxation [J]. Journal of Optimization Theory and Applications. 1999, 100(3): 699-712.
[110] Luh P. B., Zhao X., Wang Y., Lagrangian Relaxation Neural Networks for Job Shop Scheduling [J]. IEEE Transaction On Robotics and Automation, 2000, 16(1):78-88.
[111] Zhang C.S. Yan P.F., Neural network method of solving job-shop scheduling problem[J], ACTA Automation Sinica, 1995, 21: 706-712.
[112]焦李成,神经网络系统理论[M],西安:西安电子科技大学出版社,1995.
[113] Yang S., Wang D., Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-Shop Scheduling [J]. IEEE Transaction On Neural Networks. 2000, 11(2):474-486.
[114] Funahashi K., On the approximate realization of continuous mappings by neural networks[J], Neural Networks, 1989, 2(3):183-192.
[115] Archer N., Wang S., Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems[J], Decision Sciences, 1993,24(1): 60-75.
[116] Sexton R.S., Dorsey R.E., Johnson J.D., Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing [J], European Journal of OperationalResearch, 1999,114: 589-601.
[117] Arifovica J., Gencay R., Using genetic algorithms to select architecture of a feed forward artificial neural network[J], Physica A, 2001, 289:574-594.
[118] Li, Heng. Production rescheduling expert simulation system [J], European Journal of Operational Research. 2000, 124(2): 283-293.
[119] Abumaizar, R.J. Rescheduling job shops under random disruptions [J], International Journal of Production Research. 1997, 35(7):2065-2082.
[120] Hiroyasu, Toba, Segment-based approach for real-time reactive rescheduling for automatic manufacturing control[J], IEEE Transactions on Semiconductor Manufacturing, 2000,13(3): 264-272.
[121] Nessah R., Yalaoui F., Chu C. A branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates [J]. Computers & Operations Research. 2006, doi: 10.1016/j.cor.2006.07.010.
[122] Zhou X., Zhong M. Single-track train timetabling with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds [J]. Transportation Research Part B. 2006, doi: 10.1016/j.trb.2006.05.003.
[123] Crauwels H.A.J., Hariri A.M.A., Potts C.N., etc., Branch and bound algorithms for single machine scheduling with batch setup times to minimize total weighted completion time, Annals of Operations Research, 1998, 83:59-76.
[124] Dunstall S., Wirth A., A comparison of branch-and-bound algorithms for a family scheduling problem with identical parallel machines, European Journal of Operational Research, 2005, 167: 283-296.
[125] Shim S., Kim Y., A branch and bound algorithm for an identical parallel machine scheduling problem with a job splitting property, Computers & Operations Research, doi:10.1016/ j.cor.2006 .04.006.
[126]中国船舶工业行业协会网页[EB/OL],http://china.shipe.cn/Info/136467/Index.shtml
[127] Defense Modeling and Simulation Office,“HLA Interface Specification,”Version 1.3, [EB/OL]. http://www.dmso.mil/projects/hla.
[128] IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) [A].Object Model Template (OMT) Specification, 2000.
[129]续爱民,金烨,鲍劲松.虚拟场景中自动漫游路径的多分辨率规划[J],系统仿真学报,2005,17(8):1912-1915.
[130] Vinosi S., CORBA: Integrating diverse application within distributed heterogeneous environments [J]. IEEE Communication Magazine, 1997, 35(2): 45-55.
[131] Srikanth Bandi, Daniel Thalmann, Path finding for human motion in virtual environments[J], Computational Geometry, 2000, 15:103-127.
[132] Petra Jansen-Osmann, Using desktop virtual environments to investigate the role of landmarks[J], Computers in Human Behavior, 2002, 18:427-436.
[133]胡英,侯悦等,基于双距离场的三维中心路径提取算法[J],中国图象图形学报,2003,8(11):1272-1276.
[134]尚建嘎,刘修国等,三维场景交互漫游的研究和实现[J],计算机工程,2003,29(2):61-63.
[135] Wallner, Johannes: Gliding spline motions and applications[J], Computer Aided Geometric Design, January, 2004, 21(1):3-21.
[136] Hsieh Chung-Chi, B-Spline wavelet-based motion smooth[J], Computers & Industrial Engineering, 2001, 41:59-76.
[137]赵罡,穆国旺,朱心雄.基于小波的准均匀B样条曲线曲面变分造型[J].计算机辅助设计与图形学学报, 2002, 14(1): 61-65.
[138] Finkelstein A, Salesin D H. Multiresolution Curves [A]. Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH. New York: ACM Press, 1994, 261-268.
[139] Cohen A, Daubechies I, and Feauveau J C. Biorthogonal bases of compactly supported wavelets[J]. Comm. Pure Appl. Math., 1992, 45:485-560.
[140] Reissell L M, Wavelet Multiresolution Representation of Curves and Surfaces [J], Graphical Models and Image Processing, 1996, 58(3):198-217.
[141]李光明,田捷等,基于Hessian矩阵的中心路径提取算法[J],软件学报,2003,14(12): 2074-2081.
[2]李堃，现代造船工程[M].哈尔滨:哈尔滨工程大学出版社,1998.
[3]应长春,徐学光.论现代造船体制的特征和实践,中国造船,1995,(2),1-11.
[4] Kim Hongtae, Applying digital manufacturing technology to ship production and the maritime environment [J]. Integrated Manufacturing Systems. 2002, 13(5): 295-305.
[5] Lee, J.K., Lee, K.J., Hong, J.S., etc., DAS: Intelligent scheduling systems for shipbuilding [J]. AI Magazine. 1995, 16(4): 78-94.
[6] Kim H.T., Lee J.G., Lee S.S. A simulation based shipbuilding system for evaluation of validity in design and manufacturing[C]. Proceedings of IEEE International Conference on Systems, Man and Cybernetics. Washington, D.C., USA: IEEE, 2003: 522-529.
[7] Tu YiLiu. Automatic scheduling and control of a ship web welding assembly line [J]. Computer in Industry. 1996, 29(1996): 169-177.
[8] Charles McLean, Shao G. Simulation of shipbuilding operations [C]. Proceedings of the 2001 Winter Simulation Conference. Washington, D.C., USA: IEEE, 2001: 870-876.
[9] Lee, J.K, Choi, H.R., Yang, O.R., etc. Scheduling shipbuilding using a constraint directed graph search: DAS-ERECT[J]. Intelligent Systems in Accounting, Finance, and Management, 1994, 3:111-125.
[10] Park K,Lee K, Park S, et al. Modeling and solving the spatial block scheduling problem in a shipbuilding company. Computers Industry Engineering, 1996, 30(3):357-364.
[11]刘建峰.上海交通大学博士学位论文:基于PWBS船体建造运作策略研究[D].上海:上海交通大学,2000.
[12]陈强.哈尔滨工程大学博士学位论文:中心制造船模式的研究与应用[D].哈尔滨:哈尔滨工程大学,2002.
[13] Iwase, N. Rapid growth of BRICs boosts shipbuilding demand [J]. Steel Times International. 2006, 30(1): 16-16.
[14]王言英,韩国的造船厂及其建造能力简析[J].船舶. 1996, 3:56-58.
[15] Cho Kyu-Kab, Sun Jung-Guy, Oh Jung-Soo, An automated welding operation planning system for block assembly in shipbuilding, International Journal of Production Economics, 1999,60-61: 203-209.
[16] Thomas Lamb(FL), Hyun Chung(St), etc. Simulation based Performance Improvement for Shipbuilding Processes. __
[17] Lee J.K., Lee K.J., Park H.K, et al. Developing scheduling systems for Daewoo shipbuilding: DAS project [J]. European Journal of Operational Research, 1997, 97(2):380～395.
[18] Jong Gye Shin,etc.,A modeling and simulation of production process in subassembly lines at a shipyard,2001.__
[19] Aoyama, K., et al. System of Parts design and Manufacturing in Shipbuilding [J]. JSNAJ, 178: 551-561.
[20]袁崇义,Petri网原理与应用[M],北京:电子工业出版社, 2005.
[21] Srinivasan R. S., Modeling and performance analysis of cluster tools using Petri nets [J]. IEEE Transactions on Semiconductor Manufacturing, 1998(11):394–403.
[22] Lin S.Y. and Huang H.P. Modeling and emulation of a furnace in IC fab based on colored-timed Petri net[J], IEEE Transactions on Semiconductor Manufacturing. 1998(11):410–420.
[23] Wen H., Fu L.C., Huang S.S. Modeling, Scheduling, and Prediction in Wafer Fabrication Systems Using Queuing Petri Net and Genetic Algorithm[C]. Proceedings of the 2001 IEEE International Conference on Robotics & Automation, Soul, Korea, 2001:3559-3564.
[24] Chung H., Lamb T. A generic shipyard computer model- a tool for design for production [J]. Journal of Ship Production, 2000, 16(3): 160-172.
[25] Alfeld L.E., Pilliod C.S., Wilkins J. R., A Simulation Model of the Shipbuilding Process[J]. Journal of Ship Production, 14(1):33-40.
[26] IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) [A].Object Model Template (OMT) Specification, 2000.
[27]苟凌怡,熊光楞,杨流辉.支持虚拟样机的协同仿真平台关键技术研究[J],系统仿真学报, 2002,14(3): 348-355.
[28]吴哲辉, Petri网导论[M],北京:机械工业出版社, 2006.
[29] Rodrigues, Cassio L., Guerrero, Dalton D.S., etc., Model checking in object-oriented Petri nets[C], Conference Proceedings-IEEE International Conference on Systems, Man and Cybernetics, SMC, 2004, 5: 4977-4982.
[30] Boleslaw M., Charles S., Integrating object-oriented design with Petri nets - Case study of ATM system[C], Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2003, 2: 1499-1505.
[31] Lawler, E.L. Sequencing jobs to minimize total weighted completion time subject to precedence constraints [J]. Annals of Discrete Mathematics. 1978, 2: 75-90.
[32] Lenstra, J.K., Rinnooy, K.A.H. Complexity of scheduling under precedence constraints [J]. Operations Research. 1978, 100(1): 22-36.
[33] Horn, W.A. Single-machine job sequencing with treelike precedence ordering and linear delay penalties [J]. SIAM Journal on Applied Mathematics. 1972, 23: 189-202.
[34] Du, J., Leung, J.Y.T and Young, G.H. Scheduling chain structured tasks to minimize makespan and mean flow time [J]. Information and Computation. 1991, 92(2): 219-236.
[35] Han, J., Wen, J. and Zhang, G. A new approximation algorithm for UET-scheduling with chain-type precedence constraints [J]. Computers Operation Research. 1998, 25(9): 767-771.
[36] Moukrim, A. Optimal scheduling on parallel machines for a new order class [J]. Operations Research Letters. 1999, 24(1/2): 91-95.
[37] Chekuri, C., Motwani, R., Natarajan, B., etc. Approximation techniques for average completion time scheduling[C]. Proceedings of the Eighth ACM-SIAM Symposium of Discrete Algorithms. 1997: 609-618.
[38] Ramachandra, G. and Elmaghraby, S. E. Sequencing precedence-related jobs on two machines to minimize the weighted completion time [J]. International Journal of Production Economics. 2006, 100:44-58.
[39] Graham R.L. Bounds for certain multiprocessing anomalies [J]. Bell System Technical Journal.1966, 45: 1563-1581.
[40] Dell’Amico, M. and Martello, S. Optimal scheduling of tasks on identical parallel processors [J]. ORSA Journal on Computing. 1995, 7(2): 191-200.
[41] Nessah, R., Yalaoui, F. and Chu C., A Branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates [J]. Computers & Operations Research. doi: 10.1016/j.cor.2006.07.010.
[42] Franklina, M.B.T. and Vinicius, A.A. A Lagrangian-based heuristic for the capacitated lot-sizing problem in parallel machines [J]. European Journal of Operational Research. 2006, 175(2): 1070-1083.
[43] Ventura, J.A. and Kim, D. Parallel machine scheduling with earliness-tardiness penalties and additional resource constraints [J]. Computers and Operations Research. 2003, 30:1945-1958.
[44] Hou E.S.H., Ansari N. and Ren H. A genetic algorithm for multiprocessor scheduling [J]. IEEE Transactions on Parallel and Distributed Systems. 1994, 5(2):113-120.
[45] Yamauchi M., Watanabe T. A heuristic algorithm FSD for the legal firing sequence problem of Petri nets [A]. In Proc. of IEEE International Conference on Systems, Man and Cybernetics [C]. California, USA: 1998, 78-83.
[46] Matsumoto, Tadashi. Finding legal firing sequences of Petri nets by means of dynamic programming included linear programming[C]. Proceedings of the IEEE Conference on Decision and Control. 1996, 4459-4466.
[47] Matsumoto, Tadashi. Finding legal firing sequences in submarking reachability problems of Petri nets by discrete-time Pontryagin's minimum principle; Proceedings - IEEE International Symposium on Circuits and Systems, v2, Circuit Theory and Power Systems Communication and Multimedia. 1997, 1017-1020.
[48] Takahashi K., Ona L. and Satoh H., etc. An efficient genetic algorithm for reachability problems [A]. In Proceeding of 14th Hawaii International Conference on System Sciences [C], Hawaii, USA: 1997, 5(7-10) :89-98.
[49]李勇,曹广益,朱新坚,一种基于单亲遗传算法的Petri网发射路径求解算法[J].系统仿真学报. 2005, 17(1): 203-206.
[50] Yasuhisa Okumoto. Optimization of block erection using a genetic algorithm[J]. Journal of Ship Production, 2002, 18(2):116-119.
[51] Scott J.A., Todd A.S. and Sen P.,An Evolution Approach to the Scheduling of Ship Design and Production Process, 1998 Elsevier BY, :351-357.
[52] Neumann R.J. Network Scheduling Development in an MRP II Environment[J]. Journal of Ship Production, 1994, 10(4).
[53] Manivannan S., Banks, J. Real-time control of a manufacturing cell using knowledge-based simulation[C]. Proceedings of the Winter Simulation Conference, IEEE, Pos. NJ, 1991: 251-260.
[54] Bongaerts L., Monostori L., McFarlane D., etc. Hierarchy in distributed shop floor control [J]. Computers in Industry, 2000, 43: 123~137
[55] Callegari M., Palpacelli M.C., Principi M., Dynamics modelling and control of the 3-RCC translational platform[J], Mechatronics, 2006, doi:10.1016/j.mechatronics.2006.06.001.
[56] Son Changman, Optimal control planning strategies with fuzzy entropy and sensor fusion forrobotic part assembly tasks [J], International Journal of Machine Tools & Manufacture, 2002, 42: 1335–1344.
[57] Bai S.X., Gershwin S.B. Scheduling manufacturing system with work-in-process inventory control: single-part-type systems[J]. IIE Transaction. 1995, 27:599-617.
[58] Amemiya, T., Production Planning System for Oppama Shipyard. ICCAS'94, 2:12,3-14.
[59] Nakayama J., Expert Process Planning System of CIM for Shipbuilding. ICCAS'94, 2:12, :55-66.
[60]陈强,船舶制造系统的复杂性及其控制[J],中国造船,2002年第1期.
[61] Liao D., Chang S. and Pei K., etc., Daily Scheduling for R&D Semiconductor Fabrication [J]. IEEE Transactions on Semiconductor Manufacturing. 1996, 9(4):550-561
[62] Vieira G.E., Jeffrey W.H. and Edward L., Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies [J]. International Journal of Production Research, 2000, 38(8): 1899-1915.
[63] Church L.K., Uzsoy R., Analysis of periodic and event driven rescheduling policies in dynamic shops, International Journal of Computer Integrate Manufacture. 5(1992):153-163.
[64] Vieira G.E., Herrmann J.W. and Lin E., Rescheduling manufacturing systems: a framework of strategies, policies, and methods, Journal of Scheduling. 2003, 6:39–62.
[65] Kanet D., Moon I., Rescheduling algorithms in case of unit failure for batch process management [J]. Computers and Chemical Engineering, 1997, 21(S):1067-1072.
[66] Hall N.G., Potts C.N., Rescheduling for new orders, Operational Research, 2004, 52:440–453.
[67] Vallapuzha S., Meter E.C.D., Choudhuri S., etc., An investigation into the use of spatial coordinates for the genetic algorithm based solution of the fixture layout optimization problem [J]. International Journal of Machine Tools & Manufacture, 2002, 42:265–275.
[68] K. H. Lee, Intelligent Approach to a CAD system for the layout design of a ship engine room[J]. Computers industry engineering. 34(3): 599-608.
[69]张志英,江志斌,基于规则推理的自动船体装配工艺生成方法[J].上海交通大学学报. 2007, 41(1):31-35.
[70]袁崇义,Petri网原理[M],北京:电子工业出版社, 1998.
[71] Reid D.J., Constructing Petri net models using genetic search [J], Mathl. Comput. Modelling, 1998, 27(8):53-103.
[72] Reyes A., Yu H., Kelleher G. etc., Integrating Petri nets and hybrid heuristic search for the scheduling of FMS [J]. Computers in Industry, 2002, 47(2002):123-138.
[73] Lee Y.K., Park S.J., OPnets: An Object-Oriented high-level Petri net model for real-time system modeling [J], Journal of System Software, 1993, (20): 69-86
[74] Chen K.Y., A Petri-net and entity-relationship diagram based object-oriented design method for manufacturing systems control [J]. International Journal of Computer Integrated Manufacturing. 1997, 10(1-4): 17-28
[75]续爱民,金烨.基于共享合成时间Petri网的船舶吊装过程建模与仿真[J].计算机集成制造系统,2007,13(3):465-471.
[76]田锋,李人厚,张金成.基于赋时着色Petri网的协同工作建模和性能研究[J].西安交通大学学报, 2003, 37(6): 560-564.
[77]窦万春,蔡士杰.基于Petri网的工作流技术复合建模与系统分解[J].计算机集成制造系统-CIMS, 2002,8(5): 361-365.
[78] Jeng M.D., DiCesare F., A synthesis method for Petri net modeling of automated manufacturing systems with shared resources [C], Proceedings of the 31st Conference on Decision and Control, Tucson, Arizona. December 1992, 1184-1189.
[79] Ferrarini L., Trioni M., Modeling shared resources with generalized synchronization within a Petri net bottom-up approach[J], IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1996, 26(4):653-659.
[80]蒋昌骏. Petri网的行为理论及其应用[M].北京:高等教育出版社,2003.
[81]杨欣,陈丽娟,费树岷,基于柔性制造系统的Petri网模型简化规则[J].计算机集成制造系统. 2006, 12(11):1789-1793.
[82] Starke S. Reachable analysis of Petri nets using symmetries[J]. System Analysis Model Simulation. 1991, 14 (8): 293-303.
[83] Patrice B., Gerard B., Pascal A, etc. Verification technique for time Petri nets[A], IEEE International Conference on Systems, Man and Cybernetics[C]. United States: Institute of Electrical and Electronics Engineers Inc., 2004.4278-4283.
[84]姜浩,董逸生,一种基于扩展时间Petri网的工作流时间性能评价方法[J].计算机研究与发展. 2005, 42(5): 849-855.
[85] Xu Ai-min, Jin Ye., A Genetic Algorithm for Erection Scheduling at a Dock[J], Journal of Ship Production, 2007,23(1):17-22.
[86] Murata T., Ishibuchi H. and Tanaka H., Multi-objective genetic algorithm and its application to flow-shop scheduling [J]. Computers and Industrial Engineering, 1996, 30(4):957-968.
[87] Kailyanmoy Deb., Muti-Objective Optimization Using Evolutionary Algorithms [M], John Wiley & Sons Ltd, 2001.
[88] Davis L., Handbook of Genetic Algorithms [M]. New York: Van Nostrand Reinhold, 1991.
[89] Poon P.W., Carter J.N. Genetic algorithm crossover operators for ordering applications[J]. Computer Operation Resarch, 1995, 22(1):135-147.
[90] Leung Y., Gao Y., Xu Z.B. Degree of population diversity-a perspective on premature convergence in genetic algorithms and its Markov-chain analysis[J]. IEEE Trans Neural Network, 1997, 8(5): 1165-1176.
[91]王凌.车间调度及其遗传算法[M],北京:清华大学出版社,2003.
[92] Jakub W., Theoretical foundations of order-based genetic algorithms[J], Fundamenta Informaticae, 1996, 28 (3/4): 423-430.
[93] Goldberg D.E., GA in Search, Optimisation, and Machine Learning[M]. Addison-Wesley. , 1989.
[94] Rudolph G., Convergence analysis of canonical genetic algorithms[J],IEEE Transaction on neural networks, 1994, 5(1):96-101.
[95] Suzuki J.,A Markov Chain Analysis on Simple Genetic Algorithms[J],IEEE Transaction on systems, man, and cybernetics, 1995, 25(4).
[96] Leung Y., Gao Y., and Xu Z. B.,Degree of Population Diversity—A Perspective on Premature Convergence in Genetic Algorithms and its Markov Chain Analysis[J],IEEE Transactions on neural networks, 1997,8(5):1165-1176.
[97] Liu J., Cai Z X, Liu J Q, Premature Convergence in Genetic Algorithm: Analyss and PreventionBased on Chaos Operation[C], In: The Third World Congress on Intelligent Control and Automation, Hefei, China, 2000,6:495-499.
[98]王霞,周国标,遗传算法收敛率的下鞅分析[J],应用数学, 2003,16(4):130-135.
[99] Murata T., Genetic algorithms for flow shop scheduling problems[J], Computers Industry Engineering, 1996, 30(4):1061-1071.
[100] Reeves, Colin R,A Genetic algorithm for flow shop sequencing[J],Computers & Operations Research, 1995, 22(1):5-13.
[101] Tang L., Xuan H. and Liu J., A new Lagrangian relaxation algorithm for hybrid flow-shop scheduling to minimize total weighted completion time[J], Computers & Operations Research, 2006, 33: 3344-3359.
[102] Toledo F.M.B., Armentano V.A., A Lagrangian-based heuristic for the capacitated lot-sizing problem in parallel machines[J], European Journal of Operational Research, 2006, 175:1070-1083.
[103] Diaby, M., Bahl, H.C., Karwan, M.H.,etc., A Lagrangian relaxation approach for very-large-scale capacitated lot sizing. Management Science, 1992, 38:1329-1340.
[104]邢文训,谢金星,现代优化计算方法(第2版)[M],北京,清华大学出版社,2005,210-244.
[105] Luh P.B., Yu D. and Soorapanth S., A Lagrangian relaxation based approach to schedule asset overhaul and repair services[J], IEEE Transactions on Automation Science and Engineering, 2005, 2(2):145-157.
[106] Baptistea P., Flaminib M. and Sourdc F., Lagrangian bounds for just-in-time job-shop scheduling[J], Computers & Operations Research, doi:10.1016/j.cor.2006.05.009 .
[107] Chen H., Luh P.B., An alternative framework to Lagrangian relaxation approach for job shop scheduling[J]. European Journal of Operational Research. 2003, 149(2003): 499-512.
[108] Fisher M.L., The Lagrangian relaxation method for solving integer programming problems[J], Management Science, 1981, 27(1):1-18.
[109] Zhao X., Luh P. B., Wang J., Surrogate gradient algorithm for Lagrangian relaxation [J]. Journal of Optimization Theory and Applications. 1999, 100(3): 699-712.
[110] Luh P. B., Zhao X., Wang Y., Lagrangian Relaxation Neural Networks for Job Shop Scheduling [J]. IEEE Transaction On Robotics and Automation, 2000, 16(1):78-88.
[111] Zhang C.S. Yan P.F., Neural network method of solving job-shop scheduling problem[J], ACTA Automation Sinica, 1995, 21: 706-712.
[112]焦李成,神经网络系统理论[M],西安:西安电子科技大学出版社,1995.
[113] Yang S., Wang D., Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-Shop Scheduling [J]. IEEE Transaction On Neural Networks. 2000, 11(2):474-486.
[114] Funahashi K., On the approximate realization of continuous mappings by neural networks[J], Neural Networks, 1989, 2(3):183-192.
[115] Archer N., Wang S., Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems[J], Decision Sciences, 1993,24(1): 60-75.
[116] Sexton R.S., Dorsey R.E., Johnson J.D., Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing [J], European Journal of OperationalResearch, 1999,114: 589-601.
[117] Arifovica J., Gencay R., Using genetic algorithms to select architecture of a feed forward artificial neural network[J], Physica A, 2001, 289:574-594.
[118] Li, Heng. Production rescheduling expert simulation system [J], European Journal of Operational Research. 2000, 124(2): 283-293.
[119] Abumaizar, R.J. Rescheduling job shops under random disruptions [J], International Journal of Production Research. 1997, 35(7):2065-2082.
[120] Hiroyasu, Toba, Segment-based approach for real-time reactive rescheduling for automatic manufacturing control[J], IEEE Transactions on Semiconductor Manufacturing, 2000,13(3): 264-272.
[121] Nessah R., Yalaoui F., Chu C. A branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates [J]. Computers & Operations Research. 2006, doi: 10.1016/j.cor.2006.07.010.
[122] Zhou X., Zhong M. Single-track train timetabling with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds [J]. Transportation Research Part B. 2006, doi: 10.1016/j.trb.2006.05.003.
[123] Crauwels H.A.J., Hariri A.M.A., Potts C.N., etc., Branch and bound algorithms for single machine scheduling with batch setup times to minimize total weighted completion time, Annals of Operations Research, 1998, 83:59-76.
[124] Dunstall S., Wirth A., A comparison of branch-and-bound algorithms for a family scheduling problem with identical parallel machines, European Journal of Operational Research, 2005, 167: 283-296.
[125] Shim S., Kim Y., A branch and bound algorithm for an identical parallel machine scheduling problem with a job splitting property, Computers & Operations Research, doi:10.1016/ j.cor.2006 .04.006.
[126]中国船舶工业行业协会网页[EB/OL],http://china.shipe.cn/Info/136467/Index.shtml
[127] Defense Modeling and Simulation Office,“HLA Interface Specification,”Version 1.3, [EB/OL]. http://www.dmso.mil/projects/hla.
[128] IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) [A].Object Model Template (OMT) Specification, 2000.
[129]续爱民,金烨,鲍劲松.虚拟场景中自动漫游路径的多分辨率规划[J],系统仿真学报,2005,17(8):1912-1915.
[130] Vinosi S., CORBA: Integrating diverse application within distributed heterogeneous environments [J]. IEEE Communication Magazine, 1997, 35(2): 45-55.
[131] Srikanth Bandi, Daniel Thalmann, Path finding for human motion in virtual environments[J], Computational Geometry, 2000, 15:103-127.
[132] Petra Jansen-Osmann, Using desktop virtual environments to investigate the role of landmarks[J], Computers in Human Behavior, 2002, 18:427-436.
[133]胡英,侯悦等,基于双距离场的三维中心路径提取算法[J],中国图象图形学报,2003,8(11):1272-1276.
[134]尚建嘎,刘修国等,三维场景交互漫游的研究和实现[J],计算机工程,2003,29(2):61-63.
[135] Wallner, Johannes: Gliding spline motions and applications[J], Computer Aided Geometric Design, January, 2004, 21(1):3-21.
[136] Hsieh Chung-Chi, B-Spline wavelet-based motion smooth[J], Computers & Industrial Engineering, 2001, 41:59-76.
[137]赵罡,穆国旺,朱心雄.基于小波的准均匀B样条曲线曲面变分造型[J].计算机辅助设计与图形学学报, 2002, 14(1): 61-65.
[138] Finkelstein A, Salesin D H. Multiresolution Curves [A]. Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH. New York: ACM Press, 1994, 261-268.
[139] Cohen A, Daubechies I, and Feauveau J C. Biorthogonal bases of compactly supported wavelets[J]. Comm. Pure Appl. Math., 1992, 45:485-560.
[140] Reissell L M, Wavelet Multiresolution Representation of Curves and Surfaces [J], Graphical Models and Image Processing, 1996, 58(3):198-217.
[141]李光明,田捷等,基于Hessian矩阵的中心路径提取算法[J],软件学报,2003,14(12): 2074-2081.