机械制造企业生产过程的特征状态分析与控制
|
摘要
本文基于国家自然科学基金项目(No.61074135, No.50275016, No.50475154和No.50775016)中的特征状态空间理论及方法,以机械制造企业的多阶段混流生产过程为研究对象,提出并建立了生产过程的特征状态建模、分析与控制方法。以冰山集团大连冷冻机厂、大连重工·起重集团等机械制造企业的生产过程与数据为依据,通过实际算例验证了本文提出的理论与方法。
首先,本文对机械制造企业中的各段局部生产过程进行建模,定义了基本生产活动。以基本生产活动为基本分析单元,提取物料以及其它生产资源流经基本分析单元前后的时间、质量和成本等特征信息,形成特征状态矢量。根据特征状态矢量的变化规律构建基本生产活动的特征状态方程,以特征变换矩阵的形式对基本生产活动的执行情况进行直观、定量地表达。
其次,本文建立了机械制造企业整体生产过程模型。在基本生产活动特征状态方程表达基础上,针对机械制造企业中普遍存在多阶段混流生产过程,定义物料供应和设备共享两种基本生产活动之间的衔接关系。进一步以特征状态矢量的乘法与加法运算分别表达基本生产活动的串联与并联关系,根据多阶段混流生产过程中的物料流和能量流,形成了多阶段混流生产过程的特征状态方程模型。讨论了特征状态方程在计算机系统中的实现方法。
再次,本文对生产过程模型的几何意义以及与之相关的计算性质进行了讨论。定义机械制造系统的特征状态空间,讨论了空间性质。利用特征状态方程将生产过程映射到特征状态空间中研究,将机械制造系统的瓶颈分析和生产控制问题分别转化为特征状态空间中的梯度分析和路径规划问题,为这些问题的解决提供了新的方法与相应的理论依据。
然后,基于所建立的生产过程模型及其性质,介绍生产过程简化、分析与控制的具体方法,并通过算例进行验证。利用特征状态方程的符号计算实现了生产过程关键路径提取,简化了生产过程的特征状态方程描述；对简化后的方程求导,实现了制造系统瓶颈的定量分析；将瓶颈分析结果作为中长期生产控制问题的目标函数梯度,将梯度搜索策略集成进入邻域搜索算法,加速算法收敛,减少中长期生产控制问题求解时间。利用多个来源于合作企业的实际算例和仿照实际问题构造的虚拟算例,分别从测试生产过程模型精度、瓶颈分析方法性能和中长期生产控制方案优选算法性能多个方面验证了本文所提出理论与方法的可行性和有效性。与同类研究所使用的其它代表性方法对比,新方法明显提高了几种典型中长期生产控制问题的求解速度和质量,为机械制造系统的分析与控制探寻了一种新方法,开辟了一条新途径。
最后,利用本文提出的理论与方法,在为大连冷冻机厂、大连重工·起重集团等企业开发生产管理信息系统的基础上,设计并开发了网上看板控制系统,实现了生产过程的状态监测、瓶颈分析和几种典型生产控制问题的计算机辅助求解。
A new methodology for modeling, analyzing and controlling the mutil-stage and mutil-product production processes in mechanical manufacturing enterprises is developed, which is based on the characteristic state space method supported by the National Natural Science Foundation of China under Grant No.61074135, No.50775016, No.50475154 and No. 50775016. According to the production processes and the production data of Bingshan Group Dalian Refrigeration Co. and DHI-DCW Group Co., a number of case studies were provided to reveal the feasibility and validity of the new methodology.
First, local production process models are set up. Basic production activities are regarded as basic analysis units after they are defined. Characteristic state vectors are established using the variables for representing the time, the quality and the cost of the materials and the other production resources before and after those flow though the basic analysis units. Characteristic state equations are set up according to the changes of the characteristic state vectors. Then, the performance of the basic production activities are described intuitively and quantificationally in terms of characteristic transformation matrices.
Second, global production process models are set up. Two relationships of the basic production activities, which are called supplying materials and sharing capacity respectively, are defined for the mutil-stage and mutil-product production processes in mechanical manufacturing systems. The sequential relationships and the parallel relationships of the basic production activities are represented by multiplication and addition of the characteristic state vectors respectively. Then, the model of the mutil-stage and mutil-product production processes in terms of characteristic state equations is established according to the materials flows and the energy flows in the processes. The methodology for implementing the characteristic state equations on computer systems is also discussed.
Third, the geometric meaning and the associated properties for computing of the proposed production process model are discussed. The characteristic state space of a mechanical manufacturing system is defined and its properties are discussed. Based on these properties, the production processes are studied in the characteristic state space and the bottleneck analysis and the production control of the mechanical manufacturing systems are considered as gradient analysis and path-planning in the space, respectively. This study provides a new way for the bottleneck analysis and the production control and presented the relevant theories on which they are based.
Forth, based on proposed production process model and its properties, the approaches of simplifying, analyzing and controlling production processes are introduced in details. The production processes are described by simplified characteristic state equations by finding the critical paths using the symbolic computation of the equations. The bottlenecks of the mechanical manufacturing systems are measured quantificationally using the derivatives of the simplified equations. The measures of the bottlenecks are also the gradients of the object functions of the medium to long term production control problems. Consequently, the gradients can be used to guide the neighborhood-generation in a neighborhood search algorithm in order to accelerate convergence and short the run time of the neighborhood search procedure. A number of actual cases and hypothetical cases are computed for testing the production process model, the proposed bottleneck analysis method, and the modified neighborhood search algorithm. Compared to the classical methods, the computational experiments show relatively positive results. These studies pave a new way for analyzing and controlling the Production Processes in Mechanical Manufacturing Enterprises.
Finally, a web-based kanban system is designed and developed using the proposed theories and methods, based on the production management information systems of the Bingshan Group Dalian Refrigeration Co. and DHI-DCW Group Co. The production processes are monitored, the bottlenecks of the manufacturing systems are detected and measured, and several typical production control problems are solved by means of the web-based kanban system.
首先,本文对机械制造企业中的各段局部生产过程进行建模,定义了基本生产活动。以基本生产活动为基本分析单元,提取物料以及其它生产资源流经基本分析单元前后的时间、质量和成本等特征信息,形成特征状态矢量。根据特征状态矢量的变化规律构建基本生产活动的特征状态方程,以特征变换矩阵的形式对基本生产活动的执行情况进行直观、定量地表达。
其次,本文建立了机械制造企业整体生产过程模型。在基本生产活动特征状态方程表达基础上,针对机械制造企业中普遍存在多阶段混流生产过程,定义物料供应和设备共享两种基本生产活动之间的衔接关系。进一步以特征状态矢量的乘法与加法运算分别表达基本生产活动的串联与并联关系,根据多阶段混流生产过程中的物料流和能量流,形成了多阶段混流生产过程的特征状态方程模型。讨论了特征状态方程在计算机系统中的实现方法。
再次,本文对生产过程模型的几何意义以及与之相关的计算性质进行了讨论。定义机械制造系统的特征状态空间,讨论了空间性质。利用特征状态方程将生产过程映射到特征状态空间中研究,将机械制造系统的瓶颈分析和生产控制问题分别转化为特征状态空间中的梯度分析和路径规划问题,为这些问题的解决提供了新的方法与相应的理论依据。
然后,基于所建立的生产过程模型及其性质,介绍生产过程简化、分析与控制的具体方法,并通过算例进行验证。利用特征状态方程的符号计算实现了生产过程关键路径提取,简化了生产过程的特征状态方程描述；对简化后的方程求导,实现了制造系统瓶颈的定量分析；将瓶颈分析结果作为中长期生产控制问题的目标函数梯度,将梯度搜索策略集成进入邻域搜索算法,加速算法收敛,减少中长期生产控制问题求解时间。利用多个来源于合作企业的实际算例和仿照实际问题构造的虚拟算例,分别从测试生产过程模型精度、瓶颈分析方法性能和中长期生产控制方案优选算法性能多个方面验证了本文所提出理论与方法的可行性和有效性。与同类研究所使用的其它代表性方法对比,新方法明显提高了几种典型中长期生产控制问题的求解速度和质量,为机械制造系统的分析与控制探寻了一种新方法,开辟了一条新途径。
最后,利用本文提出的理论与方法,在为大连冷冻机厂、大连重工·起重集团等企业开发生产管理信息系统的基础上,设计并开发了网上看板控制系统,实现了生产过程的状态监测、瓶颈分析和几种典型生产控制问题的计算机辅助求解。
A new methodology for modeling, analyzing and controlling the mutil-stage and mutil-product production processes in mechanical manufacturing enterprises is developed, which is based on the characteristic state space method supported by the National Natural Science Foundation of China under Grant No.61074135, No.50775016, No.50475154 and No. 50775016. According to the production processes and the production data of Bingshan Group Dalian Refrigeration Co. and DHI-DCW Group Co., a number of case studies were provided to reveal the feasibility and validity of the new methodology.
First, local production process models are set up. Basic production activities are regarded as basic analysis units after they are defined. Characteristic state vectors are established using the variables for representing the time, the quality and the cost of the materials and the other production resources before and after those flow though the basic analysis units. Characteristic state equations are set up according to the changes of the characteristic state vectors. Then, the performance of the basic production activities are described intuitively and quantificationally in terms of characteristic transformation matrices.
Second, global production process models are set up. Two relationships of the basic production activities, which are called supplying materials and sharing capacity respectively, are defined for the mutil-stage and mutil-product production processes in mechanical manufacturing systems. The sequential relationships and the parallel relationships of the basic production activities are represented by multiplication and addition of the characteristic state vectors respectively. Then, the model of the mutil-stage and mutil-product production processes in terms of characteristic state equations is established according to the materials flows and the energy flows in the processes. The methodology for implementing the characteristic state equations on computer systems is also discussed.
Third, the geometric meaning and the associated properties for computing of the proposed production process model are discussed. The characteristic state space of a mechanical manufacturing system is defined and its properties are discussed. Based on these properties, the production processes are studied in the characteristic state space and the bottleneck analysis and the production control of the mechanical manufacturing systems are considered as gradient analysis and path-planning in the space, respectively. This study provides a new way for the bottleneck analysis and the production control and presented the relevant theories on which they are based.
Forth, based on proposed production process model and its properties, the approaches of simplifying, analyzing and controlling production processes are introduced in details. The production processes are described by simplified characteristic state equations by finding the critical paths using the symbolic computation of the equations. The bottlenecks of the mechanical manufacturing systems are measured quantificationally using the derivatives of the simplified equations. The measures of the bottlenecks are also the gradients of the object functions of the medium to long term production control problems. Consequently, the gradients can be used to guide the neighborhood-generation in a neighborhood search algorithm in order to accelerate convergence and short the run time of the neighborhood search procedure. A number of actual cases and hypothetical cases are computed for testing the production process model, the proposed bottleneck analysis method, and the modified neighborhood search algorithm. Compared to the classical methods, the computational experiments show relatively positive results. These studies pave a new way for analyzing and controlling the Production Processes in Mechanical Manufacturing Enterprises.
Finally, a web-based kanban system is designed and developed using the proposed theories and methods, based on the production management information systems of the Bingshan Group Dalian Refrigeration Co. and DHI-DCW Group Co. The production processes are monitored, the bottlenecks of the manufacturing systems are detected and measured, and several typical production control problems are solved by means of the web-based kanban system.
引文
[1]杨叔子,吴波,李斌.再论先进制造技术及其发展趋势[J].机械工程学报,2006,42(1)：1-5.
[2]王丽亚.生产计划与控制[M].北京：清华大学出版社,2007.
[3]王德伦,张德珍,马雅丽.机械运动方案设计的状态空间方法[J].机械工程学报,2003,39(3)：22-27.
[4]张利萍,王德伦.混联机械系统方案设计特征状态空间理论与方法[J].机械工程学报,2006,42(12)：26-35.
[5]马文勇,王德伦.液压系统方案设计的能量特征状态模型[J].机械工程学报,2010,46(4)：129-135.
[6]Wenyong Ma, Delun Wang, Kwun-lon Ting. Characteristic Matrices and Conceptual Design of Hydraulic Systems[J]. Journal of Mechanical Design,2010,132(3): 031005-1-031005-13.
[7]李朝林.网上看板与精益生产控制方式[J].工业工程,2008,11(12)：25-31.
[8]殷保群,奚宏生,周亚平.排队系统性能分析与Markov控制过程[M].合肥：中国科学技术大学出版社,2004.
[9]Gabriel R. Bitran, Sriram Dasu. A review of open queueing network models of manufacturing systems[J]. Queueing Systems,1992,12:95-134.
[10]John A. Buzacott, J. George Shanthikuma. Design of manufacturing systems using queueing models. Queueing Systems,1992,12:135-214.
[11]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part1:a framework[J]. International Journal of Production Research,2000,38(10):2273-2285.
[12]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part2:Optimal configurations[J]. International Journal of Production Research,2000,38(12):2829-2842.
[13]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part3:Optimal selection policy [J]. International Journal of Production Research,2001,39(4):747-758.
[14]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part4:Performance measure[J]. International Journal of Production Research,2001,39(6):1113-1126.
[15]楼洪梁,杨将新,林亚福,等.单零件族可重构制造系统的生产能力优化设计[J].农业机械学报,2007,38(2)：125-128.
[16]楼洪梁,杨将新,林亚福,等.双零件族随机环境下可重构制造系统的生产能力设计[J].计算机集成制造系统,2007,13(1)：13-17.
[17]Seshadri S, Pinedo M. Optimal allocation of resources in a job shop environment [J]. HE Transactions,1999,31(3):195-206.
[18]Souza G S, Ketzenberg M E, Guide VDR. Capacitated remanufacturing with service level constraints[J]. Production and Operations management,2002,11(2):231-248.
[19]Silva C R, Morabito R. Performance evaluation and capacity planning in a metallurgical job-shop system using open queueing network models[J]. International Journal of Production Research,2009,47(23):6589-6609.
[20]李培根.制造系统性能分析建模[M].武昌：华中理工大学出版社,1998.
[21]Peter Buchholz. An adaptive aggregation/disaggregation algorithm for hierarchical Markovian models [J]. European Journal of Operational Research,1999,116(3):545-564.
[22]张鸣,赵晓波,罗振璧,盛伯浩.可重组柔性制造系统中的最优工件运送策略[J].清华大学学报(自然科学版),1999,39(8)：41-44.
[23]盛伯浩,罗振璧,俞圣梅等.快速重组制造系统的动态建模和结构体系[J].中国机械工程,1999,10(9)：105-106.
[24]Cohen G. A linear system theoretic view of discrete event processes and its use for performance evolution in manufacturing. IEEE Trans. on Automatic Control,1985,30(3): 210-220.
[25]陈文德,齐向东.离散事件动态系统——极大代数方法[M].北京：科学出版社,1994.
[26]陈文德,张仁忠.带有限缓冲器的多出口串行生产线无阻塞最优控制及性能分析[J].系统工程理论与实践,2000,12：84-90.
[27]张仁忠,陈文德.多入口多出口串行生产线的最优节奏控制[J].系统科学与数学,2004,24(1)：1-9.
[28]戴华平.多尺度条件下制造系统的监控器设计——极大代数方法[J].系统工程理论与实践,2005,6：49-53.
[29]J. L. Bouquard, C. Lente, J. C. Billaut. Application of an optimization problem in Max-Plus algebra to scheduling problems[J]. Discrete Applied Mathematics, 2006,154(15):2064-2079.
[30]江志斌.Petri网及其在制造系统建模与控制中的应用[M].北京：机械工业出版社,2004.
[31]袁崇义.Petri网原理与应用[M].北京：电子工业出版社,2005.
[32]林闯.随机Petri网和系统性能评价[M].北京：清华大学出版社,2005.
[33]吴哲辉.Petri网导论[M].北京：机械工业出版社,2006.
[34]Narciso M E, Piera M A. Petri net simulator:A generic tool for production planing [A]. IEEE Symposium on Emerging Technologies and Factory Automation,8th International Conference on Emerging Technologies and Factory Automation,2001:139-147.
[35]Bayhan G M, Tuncel G. Modeling, behavioral analysis and performance evaluation of an automotive assembly plant using Petri nets[J]. International Journal of Industrial Engineering:Theory Applications and Practice.2002,9(3):238-247.
[36]王琴,钱钧,张伟军,杨汝清.基于规则嵌入赋色Petri网模型的柔性装配系统仿真[J].上海交通大学学报,2004,38(8)：1341-1345.
[37]韩赞东,熊道权.混合PetriNet在制造系统建模与仿真中的应用[J].清华大学学报(自然科学版),2002,(11).
[38]王芳,徐德,任思成,谭民.可重构制造系统的Petri网建模和分析方法[J].控制与决策,2005,(6).
[39]田国会,张攀,田伟,李晓磊等.基于层次着色Petri网的物资储配系统调度问题研究[J].机械工程学报,2005,41(4)：148-153.
[40]幸研,易红,汤文成.基于GSPN的过程建模分析方法研究[J].机械工程学报,2004,(3).
[41]谢楠,李爱平,徐立云.基于广义随机Petri网的可重组制造单元建模与分析方法[J].计算机集成制造系统,2006,12(6)：828-834.
[42]谢楠,李爱平.基于扩展随机Petri网的可重组制造系统建模与分析方法[J].机械工程学报,2006,42(12)：224-231.
[43]战德臣,王忠杰,李小平,刘若昕.基于物料清单工艺清单的网络图绘制方法[J].中国机械工程,2003,14(2)：120-125.
[44]周万坤,朱剑英.面向制造过程的工作流建模[J].机械科学与技术,2004,23(2)：230-233.
[45]何炎祥,陈莘萌.Agent和多Agent系统的设计与应用[M].武汉：武汉大学出版社,2001.
[46]Yungyu Chen, Lichen Fu, Yuchien Chen. Multi-agent based dynamic scheduling for a flexible assembly system[A]. International Conference on Robotics and Automation, 1998:2122-2126.
[47]王晓芳,杨家本.制造系统中任务分配的自适应Agent模型[J].计算机集成制造系统.2001,13(8)：17-20.
[48]赵骥,肖田元,韩向利,董建华,赵银燕.制造系统虚拟环境行为建模研究[J].清华大学学报(自然科学版),2002,(3).
[49]李琳,江志斌.虚拟生产系统的自适应动态调度机理及算法[J].计算机集成制造系统,2006,12(9)：1444-1452.
[50]刘波,罗军舟,宋爱波.基于颜色Petri网的多agent动态调度建模与分析[J].系统仿真学报,2007,19(1):193-198.
[51]Wang Q, Chatwin CR. Key issues and developments in modelling and simulation-based methodologies for manufacturing systems analysis, design and performance evaluation[J]. International Journal of Advanced Manufacturing Technology,2005, 25(11-12):1254-1265.
[52]王国新,宁汝新,王爱民,李海.仿真优化在制造系统中的应用现状及发展趋势[J].系统仿真学报,2008,20(1)：1-6.
[53]杨宏兵,严洪森.知识化制造系统中动态调度的自适应策略研究[J].控制与决策,2002,22(12)：1335-1340.
[54]宋士刚,李爱平,徐立云.可重组制造系统缓冲区容量的优化研究[J].计算机集成制造系统,2008,14(10)：1951-1955.
[55]Gert Z. Staff oriented simulation of new production systems[J]. International Journal of Production Economics,1991,24(1-2):199-206.
[56]Gatland R, Yang E, Buxton K. Solving engine maintenance capacity problems with simulation[A]. Proceedings of the 1997 Winter Simulation Conference, Atlanta, USA, 1997:892-899.
[57]Arer M M, Ozdemirel N E. Simulation of capacity expansion and sequencing alternatives for a sheet metal producer[A]. Journal of the Operational Research Society,1999,50(6): 596-607.
[58]Choi S H, Lee SL. The design architecture and implementation of an integrated manufacturing system, International Journal of Computer Integrated Manufacturing, 2000,10(1-4):232-244.
[59]Kim C H, Weston R. Development of an integrated methodology for enterprise engineering[J]. International Journal of Computer Integrated Manufacturing,2001, 14(5):473-488.
[60]Byrne M D, Bakir M A. Production planning using a hybrid simulation-analytical approach[J]. International Journal of Production Economics,1999,59(1):305-311
[61]Byrne MD, Bakir MA. Production planning using a hybrid simulation-analytical approach[J]. International Journal of Production Economics,1999,59(1):305-311.
[62]Kim B, Kim S. Extended model for a hybrid production planning approach[J]. International Journal of Production Economics,2001,73(2):165-173.
[63]Arakawa M, Fuyuki M, Nakanishi H, Inoue I. A simulation-based capacity adjustment method for job shop production scheduling [A]. Proceedings of the Third Asia-Pacific Conference on Industrial Engineering and Management Systems (APIEMS'2000), Hong Kong, China,2000:84-90.
[64]Arakawa M, Fuyuki M, Nakanishi H. An optimization-oriented method for simulation-based job shop scheduling incorporating capacity adjustment function[J]. International Journal of Production Economics,2003,85(3):359-369.
[65]Yang Y H, Zhao F Q, Hong Y, Yu D M. Integration of process planning and production scheduling with particle swarm optimization (PSO) algorithm and fuzzy inference systems[A]. ICMIT 2005:Control Systems and Robotics, Chongqing, China,2005: 2292-2297.
[66]Jarugumilli S, Fu MY, Keng NP, Dejong C, et al. Framework for execution level capacity allocation decisions for assembly:test facilities using integrated optimization-simulation models[A]. Proceedings of the 40th Conference on Winter Simulation, Miami, USA,2008:2292-2297.
[67]Shahabudee P, Krishnaiah K. Design of a Bi-Criteria kanban system using Genetic Algorithm[J]. International Journal of Management system,1999,15(3):257-274.
[68]Shahabudee P, Krishnaiah K, Narayanan MT. Design of a Two-Card Dynamic Kanban System Using a Simulated Annealing Algorithm[J]. International Journal of Advanced Manufacturing Technology,2003,21(10-11):754-759.
[69]郑大钟,赵千川.离散事件动态系统[M].北京：清华大学出版社,2001.
[70]何霆,刘飞,马玉林,等.车间生产调度问题研究[J].机械工程学报,2000,36(5)：97-102.
[71]金锋,吴澄.大规模生产调度问题的研究现状与展望[J].计算机集成制造系统,2006,12(2)：161-168.
[72]Panwalkar S. A survey of scheduling roles [J]. Operation Research,1977,25(1):45-61.
[73]Sarper H, Henry M. Combinatorial evaluation of six dispatching rules in a dynamic two-machine flow shop[J]. Omega,1996,24(1):73-81.
[74]Yang B, Geunes J, OBrien W J. A heuristic approach for minimizing weighted tardiness and overtime costs in single resource scheduling[J]. Computers and Operations Research, 2004,31(8):1273-1301.
[75]Mosheiov G, Oron D. A note on the SPT heuristic for solving scheduling problems with generalized due dates[J]. Computers and Operations Research,2004,31(5):645-655.
[76]Jayamohan M S, Rajendran C. Development and analysis of cost-based dispatching rules for job shop scheduling. European Journal of Operational Research,2004,157(2): 307-321
[77]Prabaharan T, Nakkeeran P R, Jawahar N. Sequencing and scheduling of job and tool in a flexible manufacturing cell[J]. International Journal of Advanced Manufacturing Technology,2006,29(7-8):729-745.
[78]熊禾根,李建军,孔建益,等.考虑工序相关性的动态Job shop调度问题启发式算法[J].机械工程学报,2006,42(8)：50-55.
[79]Leung J, Li H B, Pinedo M, et al. Minimizing total weighted completion time when scheduling orders in a flexible environment with uniform machines[J]. Information Processing Letters,2007,103(3):119-129.
[80]Vinod V, Sridharan R. Dynamic job-shop scheduling with sequence-dependent setup times simulation modeling and analysis[J]. International Journal of Advanced Manufacturing Technology,2008,36(3-4):355-372.
[81]Natarajan K, Mohanasundaran K M, Babu B S, Suresh S, et al. Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flow time and tardiness[J]. International Journal of Advanced Manufacturing Technology,2007,31(7-8):751-761.
[82]Masahiko A. An artificial neural net work optimized by a genetic algorithm for real time Flow shop scheduling[A]. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies,2000:329-332.
[83]Michecel J S. A neural net approach to real time Flow shop sequencing[J]. Computer & Industrial Engineering,2000,38(2):125-147.
[84]李树刚,吴智铭,庞小红.Job-shop的实时调度[J].上海交通大学学报,2004,38(4)：559-563.
[85]杨宏兵,严洪森.知识化制造系统中动态调度的自适应策略研究[J].控制与决策,2007,22(12)：1335-1346.
[86]Adams J, Balas E, Zawack D. The shifting bottleneck procedure for job shop scheduling[J]. Management Science,1988,34(3):391-401.
[87]Applegate D, Cook W. A computational study of job shop scheduling[J]. ORSA Journal of Computing,1991,3(2):149-156.
[88]Dauzere-Peres S, Lasserre J B. A modified shifting bottleneck procedure for job shop scheduling[J]. International Journal of Production Research,1993,31(4):923-932.
[89]Balas E, Lenstra J, Vazacopoulos A. The one-machine problem with delayed precedence constraints and its use in the job shop scheduling[J]. Management Science,1995,41(1): 94-109.
[90]Ramudhin A, Philip M. The generalized shifting bottleneck procedure[J]. European Journal of Operational Research,1996,93(1):34-48.
[91]Pinedo M, Singer M. A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop[J]. Naval Research and Logistics 1999,46:1-17.
[92]王凌.车间调度及其遗传算法[M].北京：清华大学出版社,2003.
[93]Jawahar N, Aravindan P, Ponnambalam S G. A Genetic Algorithm for Scheduling Flexible Manufacturing Systems [J]. International Journal of Advanced Manufacturing Technology, 1998,14(8):588-607.
[94]Bierwirth C, Mattfeld D C. Production scheduling and rescheduling with genetic algorithms[J]. Evolutionary Computation,1999,7(1):1-17.
[95]姜思杰,徐晓飞,李全龙.基于遗传优化算法求解作业车间调度问题[J].计算机集成制造系统,2002,8(3)：229-232.
[96]张超勇,饶运清,李培根,等.柔性作业车间调度问题的两级遗传[J].机械工程学报,2007,43(4)：119-124.
[97]柴永生,孙树栋,余建军,吴秀丽.基于免疫遗传算法的车间动态调度[J].机械工程学报,2005,41(10)：24-27.
[98]吴秀丽,孙树栋,郝京辉,等.面向成本的车间调度优化模型研究[J].机械科学与技术,2006,25(4)：421-425.
[99]宋扬,张智海,郑力.蚁群算法求解独立到达时间单机提前/拖期调度问题[J].清华大学学报(自然科学版),2005,45(11)：1577-1580.
[100]郜庆路,罗欣,杨叔子.基于蚂蚁算法的混流车间动态调度研究[J].计算机集成制造系统,2003,9(6)：456-475.
[101]Liu Y, Ye C, Chen B. Improved particle swarm optimization for flexible job-shop scheduling problem[J]. Journal of Computational Information Systems,2005,1(4): 693-699.
[102]林中伟,谢强,丁秋林,宁福臣.基于遗传算法的多级有限能力生产计划[J].小型微型计算机系统,2003,24(1)：123-125.
[103]周泓,谭小卫.一种两层生产计划问题建模及其遗传算法设计[J].系统仿真学报,2007,19(6)：3643-3649.
[104]孙明贵,李志远,金梅,李焱.库存物流管理[M].北京：中国社会科学出版社,2005.
[105]Sven Axsater. Inventory control[M]. 北京：清华大学出版社，2007.
[106]周永务,王圣东.库存控制理论与方法[M].北京：科学出版社,2009.
[107]Blair J, Berkley. A review of the kanban production control research literature[J]. Production and Operations Management,1992,1(4):393-411.
[108]Sendil Kumar C, Panneerselvam R. Literature review of JIT-KANBAN system[J]. International Journal of Advanced Manufacturing Technology,2007,32(3-4):393-408.
[109]曹振新,朱云龙.混流轿车总装配线上物料配送的研究与实践[J].计算机集成制造系统,2006,12(2)：285-291.
[110]Hung-da Wan, F. Frank Chen. A Web-based Kanban system for job dispatching, tracking, and performance monitoring[J]. International Journal of Advanced Manufacturing Technology,2008,38(9-10):995-1005.
[111]Towill D R. Dynamic analysis of an inventory and order based production control system[J]. International Journal of Production Research,1982,20(6):671-687.
[112]John S, Naim M M, Towill D R. Dynamic analysis of a WIP compensated decision support system[J]. International Journal of Manufacturing System Design,1994,1(1):283-297.
[113]Zhou L, Naim M M, Tang 0, Towill D R. Dynamic performance of a hybrid inventory system with a Kanban policy in remanufacturing process[J]. Omega,2006,34(6):585-598.
[114]陈淮莉,马登哲,张洁.基于APIOBPCS的供应链生产控制方法[J].机械工程学报,2005,41(10)：137-142.
[115]陈淮莉,马登哲,张洁.基于库存反馈补偿的生产控制及工程应用[J].上海交通大学学报,2005,39(9)：1396-1399.
[116]罗卫,张子刚,欧阳明德.基于DE-APIOBPCS策略的牛鞭效应和库存方差[J].中国管理科学.2005,13(2)：88-94.
[117]罗卫,张子刚,欧阳明德.基于APIOBPCS模型的定购决策引起的牛鞭效应[J].统计与决策,2005,164(4)：34-36.
[118]Berkley B J. A simulation study of container size in two-card kanban system[J]. International Journal of Production Research,1996,34(12):3417-3445.
[119]Salomon R. Evolutionary algorithms and gradient search:similarities and differences[J]. IEEE Transactions on Evolutionary Computation,1998,2(2):45-55.
[120]何新贵,梁久祯.利用目标函数梯度的遗传算法[J].软件学报,2001,12(7)：981-985.
[121]Peng JX, Thompson S, Li K. A gradient-guided niching method in genetic algorithm for solving continuous optimisation problems [A]. Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, China,2002:3333-3338.
[122]Yiu KFC, Liu Y, Teo KL A hybrid descent method for global optimization[J]. Journal of Global Optimization,2004,28(2):229-238.
[123]吴韫章.自动控制理论基础[M].西安：西安交通大学出版社,1999.
[124]钟秋海.现代控制理论[M].北京：高等教育出版社,2004.
[125]陆文.控制理论基础[M].北京：清华大学出版社,2008.
[126]曹德芳,周黎明.成本管理学[M].沈阳：东北大学出版社,2003.
[127]孙茂竹,姚岳.成本管理学[M].北京：中国人民大学出版社,2003.
[128]Cooper R, Kaplan R S.. How Cost Accounting Distorts Product Costs[J]. Management Accounting,1988,69(10):20-27.
[129]Cooper R, Kaplan R S. The design of cost management systems[M]. Englewood Cliffs: Prentice Hill,1991.
[130]Sunnel M. Activity Based Costing System[J]. Management Accounting,1995,76(9): 649-656.
[131]Cokins G. Activity-based Cost Management Making It Works[M]. New York:McGraw-Hill, 1997.
[132]王平心.作业成本计算理论与应用研究[M].大连：东北财经大学出版社,2001.
[133]栾庆伟.成本管理新模式[D]：(博士学位论文).大连：大连理工大学,2001.
[134]邓为民.作业成本管理系统关键技术研究[D]：(博士学位论文).西安：西北工业大学,2003.
[135]李从东,汤勇力,张洪亮,等.基于广义工作中心的作业成本法及应用[J].计算机集成制造系统,2005,11(2)：228-232.
[136]杨继良.成本会计的新方法——资源消耗会计概述[J].财会通讯,2005,(4)：22-26.
[137]白明.成本管理系统的创新资源消耗会计[J].中国管理信息化(综合版),2005,(7)：2-3.
[138]David D, Colwyn J. Throughput Accounting:Transforming Practices[J]. The British Accounting Review,1998,30(3):203-220.
[139]黄亮,王德伦,唐苏州.基于投入产出模型的制造成本计算系统研究[J].计算机集成制造系统,2007,13(11)：2133-2139.
[140]唐苏州,王德伦,黄亮.基于分层动态映射的产品成本预测方法[J].计算机集成制造系统,2010,16(2)：365-371.
[141]Cuninghame-Green R. Minimax algebra Lecture notes in economics and mathematical systems Vol.166[M]. New York:Springer-Verlag,1979.
[142]王玉荣.瓶颈管理：TOC[M].北京：机械工业出版社,2002.
[143]Lawrence S R, Buss A H. Economic analysis of production bottlenecks[J]. Mathematical Problems in Engineering,1995,1(4):341-369.
[144]Moss H K, Yu W B. Toward the estimation of bottleneck shiftiness in a manufacturing operation[J]. Production and Inventory Management Journal,1999,40(2):53-58.
[145]C. Roser, M. Nakano, M. Tanaka. Shifting bottleneck detection[A]. Proceedings of the 2002 Winter Simulation Conference, New York, USA,2002:1079-1086.
[146]Michhalewicz Z. Genetic algorithm+data structure= evolution program 3rd ed[M]. New York:Springer-Verlag,1999.
[147]Kirkpatrick S, Gelatt C D Jr, Vecchi M P. Optimization by simulated annealing[J]. Science,1983,220(4598):671-680
[148]Law A M, Kelton W D. Simulation modeling and analysis.2nd ed[M]. New York: McGraw-Hill,1991.
[149]方开泰,马长兴.正交与均匀试验设计[M].北京：科学出版社,2001.
[150]钱积新,赵均,徐祖华.预测控制[M].北京：化学工业出版社,2007.
[151]李桂华,张建华,周红.统计学[M].北京：清华大学出版社,2008.
[2]王丽亚.生产计划与控制[M].北京：清华大学出版社,2007.
[3]王德伦,张德珍,马雅丽.机械运动方案设计的状态空间方法[J].机械工程学报,2003,39(3)：22-27.
[4]张利萍,王德伦.混联机械系统方案设计特征状态空间理论与方法[J].机械工程学报,2006,42(12)：26-35.
[5]马文勇,王德伦.液压系统方案设计的能量特征状态模型[J].机械工程学报,2010,46(4)：129-135.
[6]Wenyong Ma, Delun Wang, Kwun-lon Ting. Characteristic Matrices and Conceptual Design of Hydraulic Systems[J]. Journal of Mechanical Design,2010,132(3): 031005-1-031005-13.
[7]李朝林.网上看板与精益生产控制方式[J].工业工程,2008,11(12)：25-31.
[8]殷保群,奚宏生,周亚平.排队系统性能分析与Markov控制过程[M].合肥：中国科学技术大学出版社,2004.
[9]Gabriel R. Bitran, Sriram Dasu. A review of open queueing network models of manufacturing systems[J]. Queueing Systems,1992,12:95-134.
[10]John A. Buzacott, J. George Shanthikuma. Design of manufacturing systems using queueing models. Queueing Systems,1992,12:135-214.
[11]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part1:a framework[J]. International Journal of Production Research,2000,38(10):2273-2285.
[12]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part2:Optimal configurations[J]. International Journal of Production Research,2000,38(12):2829-2842.
[13]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part3:Optimal selection policy [J]. International Journal of Production Research,2001,39(4):747-758.
[14]Zhao Xiaobao, Wang Jiancai, Luo Zhenbi. A stochastic model of a reconfigurable manufacturing system-part4:Performance measure[J]. International Journal of Production Research,2001,39(6):1113-1126.
[15]楼洪梁,杨将新,林亚福,等.单零件族可重构制造系统的生产能力优化设计[J].农业机械学报,2007,38(2)：125-128.
[16]楼洪梁,杨将新,林亚福,等.双零件族随机环境下可重构制造系统的生产能力设计[J].计算机集成制造系统,2007,13(1)：13-17.
[17]Seshadri S, Pinedo M. Optimal allocation of resources in a job shop environment [J]. HE Transactions,1999,31(3):195-206.
[18]Souza G S, Ketzenberg M E, Guide VDR. Capacitated remanufacturing with service level constraints[J]. Production and Operations management,2002,11(2):231-248.
[19]Silva C R, Morabito R. Performance evaluation and capacity planning in a metallurgical job-shop system using open queueing network models[J]. International Journal of Production Research,2009,47(23):6589-6609.
[20]李培根.制造系统性能分析建模[M].武昌：华中理工大学出版社,1998.
[21]Peter Buchholz. An adaptive aggregation/disaggregation algorithm for hierarchical Markovian models [J]. European Journal of Operational Research,1999,116(3):545-564.
[22]张鸣,赵晓波,罗振璧,盛伯浩.可重组柔性制造系统中的最优工件运送策略[J].清华大学学报(自然科学版),1999,39(8)：41-44.
[23]盛伯浩,罗振璧,俞圣梅等.快速重组制造系统的动态建模和结构体系[J].中国机械工程,1999,10(9)：105-106.
[24]Cohen G. A linear system theoretic view of discrete event processes and its use for performance evolution in manufacturing. IEEE Trans. on Automatic Control,1985,30(3): 210-220.
[25]陈文德,齐向东.离散事件动态系统——极大代数方法[M].北京：科学出版社,1994.
[26]陈文德,张仁忠.带有限缓冲器的多出口串行生产线无阻塞最优控制及性能分析[J].系统工程理论与实践,2000,12：84-90.
[27]张仁忠,陈文德.多入口多出口串行生产线的最优节奏控制[J].系统科学与数学,2004,24(1)：1-9.
[28]戴华平.多尺度条件下制造系统的监控器设计——极大代数方法[J].系统工程理论与实践,2005,6：49-53.
[29]J. L. Bouquard, C. Lente, J. C. Billaut. Application of an optimization problem in Max-Plus algebra to scheduling problems[J]. Discrete Applied Mathematics, 2006,154(15):2064-2079.
[30]江志斌.Petri网及其在制造系统建模与控制中的应用[M].北京：机械工业出版社,2004.
[31]袁崇义.Petri网原理与应用[M].北京：电子工业出版社,2005.
[32]林闯.随机Petri网和系统性能评价[M].北京：清华大学出版社,2005.
[33]吴哲辉.Petri网导论[M].北京：机械工业出版社,2006.
[34]Narciso M E, Piera M A. Petri net simulator:A generic tool for production planing [A]. IEEE Symposium on Emerging Technologies and Factory Automation,8th International Conference on Emerging Technologies and Factory Automation,2001:139-147.
[35]Bayhan G M, Tuncel G. Modeling, behavioral analysis and performance evaluation of an automotive assembly plant using Petri nets[J]. International Journal of Industrial Engineering:Theory Applications and Practice.2002,9(3):238-247.
[36]王琴,钱钧,张伟军,杨汝清.基于规则嵌入赋色Petri网模型的柔性装配系统仿真[J].上海交通大学学报,2004,38(8)：1341-1345.
[37]韩赞东,熊道权.混合PetriNet在制造系统建模与仿真中的应用[J].清华大学学报(自然科学版),2002,(11).
[38]王芳,徐德,任思成,谭民.可重构制造系统的Petri网建模和分析方法[J].控制与决策,2005,(6).
[39]田国会,张攀,田伟,李晓磊等.基于层次着色Petri网的物资储配系统调度问题研究[J].机械工程学报,2005,41(4)：148-153.
[40]幸研,易红,汤文成.基于GSPN的过程建模分析方法研究[J].机械工程学报,2004,(3).
[41]谢楠,李爱平,徐立云.基于广义随机Petri网的可重组制造单元建模与分析方法[J].计算机集成制造系统,2006,12(6)：828-834.
[42]谢楠,李爱平.基于扩展随机Petri网的可重组制造系统建模与分析方法[J].机械工程学报,2006,42(12)：224-231.
[43]战德臣,王忠杰,李小平,刘若昕.基于物料清单工艺清单的网络图绘制方法[J].中国机械工程,2003,14(2)：120-125.
[44]周万坤,朱剑英.面向制造过程的工作流建模[J].机械科学与技术,2004,23(2)：230-233.
[45]何炎祥,陈莘萌.Agent和多Agent系统的设计与应用[M].武汉：武汉大学出版社,2001.
[46]Yungyu Chen, Lichen Fu, Yuchien Chen. Multi-agent based dynamic scheduling for a flexible assembly system[A]. International Conference on Robotics and Automation, 1998:2122-2126.
[47]王晓芳,杨家本.制造系统中任务分配的自适应Agent模型[J].计算机集成制造系统.2001,13(8)：17-20.
[48]赵骥,肖田元,韩向利,董建华,赵银燕.制造系统虚拟环境行为建模研究[J].清华大学学报(自然科学版),2002,(3).
[49]李琳,江志斌.虚拟生产系统的自适应动态调度机理及算法[J].计算机集成制造系统,2006,12(9)：1444-1452.
[50]刘波,罗军舟,宋爱波.基于颜色Petri网的多agent动态调度建模与分析[J].系统仿真学报,2007,19(1):193-198.
[51]Wang Q, Chatwin CR. Key issues and developments in modelling and simulation-based methodologies for manufacturing systems analysis, design and performance evaluation[J]. International Journal of Advanced Manufacturing Technology,2005, 25(11-12):1254-1265.
[52]王国新,宁汝新,王爱民,李海.仿真优化在制造系统中的应用现状及发展趋势[J].系统仿真学报,2008,20(1)：1-6.
[53]杨宏兵,严洪森.知识化制造系统中动态调度的自适应策略研究[J].控制与决策,2002,22(12)：1335-1340.
[54]宋士刚,李爱平,徐立云.可重组制造系统缓冲区容量的优化研究[J].计算机集成制造系统,2008,14(10)：1951-1955.
[55]Gert Z. Staff oriented simulation of new production systems[J]. International Journal of Production Economics,1991,24(1-2):199-206.
[56]Gatland R, Yang E, Buxton K. Solving engine maintenance capacity problems with simulation[A]. Proceedings of the 1997 Winter Simulation Conference, Atlanta, USA, 1997:892-899.
[57]Arer M M, Ozdemirel N E. Simulation of capacity expansion and sequencing alternatives for a sheet metal producer[A]. Journal of the Operational Research Society,1999,50(6): 596-607.
[58]Choi S H, Lee SL. The design architecture and implementation of an integrated manufacturing system, International Journal of Computer Integrated Manufacturing, 2000,10(1-4):232-244.
[59]Kim C H, Weston R. Development of an integrated methodology for enterprise engineering[J]. International Journal of Computer Integrated Manufacturing,2001, 14(5):473-488.
[60]Byrne M D, Bakir M A. Production planning using a hybrid simulation-analytical approach[J]. International Journal of Production Economics,1999,59(1):305-311
[61]Byrne MD, Bakir MA. Production planning using a hybrid simulation-analytical approach[J]. International Journal of Production Economics,1999,59(1):305-311.
[62]Kim B, Kim S. Extended model for a hybrid production planning approach[J]. International Journal of Production Economics,2001,73(2):165-173.
[63]Arakawa M, Fuyuki M, Nakanishi H, Inoue I. A simulation-based capacity adjustment method for job shop production scheduling [A]. Proceedings of the Third Asia-Pacific Conference on Industrial Engineering and Management Systems (APIEMS'2000), Hong Kong, China,2000:84-90.
[64]Arakawa M, Fuyuki M, Nakanishi H. An optimization-oriented method for simulation-based job shop scheduling incorporating capacity adjustment function[J]. International Journal of Production Economics,2003,85(3):359-369.
[65]Yang Y H, Zhao F Q, Hong Y, Yu D M. Integration of process planning and production scheduling with particle swarm optimization (PSO) algorithm and fuzzy inference systems[A]. ICMIT 2005:Control Systems and Robotics, Chongqing, China,2005: 2292-2297.
[66]Jarugumilli S, Fu MY, Keng NP, Dejong C, et al. Framework for execution level capacity allocation decisions for assembly:test facilities using integrated optimization-simulation models[A]. Proceedings of the 40th Conference on Winter Simulation, Miami, USA,2008:2292-2297.
[67]Shahabudee P, Krishnaiah K. Design of a Bi-Criteria kanban system using Genetic Algorithm[J]. International Journal of Management system,1999,15(3):257-274.
[68]Shahabudee P, Krishnaiah K, Narayanan MT. Design of a Two-Card Dynamic Kanban System Using a Simulated Annealing Algorithm[J]. International Journal of Advanced Manufacturing Technology,2003,21(10-11):754-759.
[69]郑大钟,赵千川.离散事件动态系统[M].北京：清华大学出版社,2001.
[70]何霆,刘飞,马玉林,等.车间生产调度问题研究[J].机械工程学报,2000,36(5)：97-102.
[71]金锋,吴澄.大规模生产调度问题的研究现状与展望[J].计算机集成制造系统,2006,12(2)：161-168.
[72]Panwalkar S. A survey of scheduling roles [J]. Operation Research,1977,25(1):45-61.
[73]Sarper H, Henry M. Combinatorial evaluation of six dispatching rules in a dynamic two-machine flow shop[J]. Omega,1996,24(1):73-81.
[74]Yang B, Geunes J, OBrien W J. A heuristic approach for minimizing weighted tardiness and overtime costs in single resource scheduling[J]. Computers and Operations Research, 2004,31(8):1273-1301.
[75]Mosheiov G, Oron D. A note on the SPT heuristic for solving scheduling problems with generalized due dates[J]. Computers and Operations Research,2004,31(5):645-655.
[76]Jayamohan M S, Rajendran C. Development and analysis of cost-based dispatching rules for job shop scheduling. European Journal of Operational Research,2004,157(2): 307-321
[77]Prabaharan T, Nakkeeran P R, Jawahar N. Sequencing and scheduling of job and tool in a flexible manufacturing cell[J]. International Journal of Advanced Manufacturing Technology,2006,29(7-8):729-745.
[78]熊禾根,李建军,孔建益,等.考虑工序相关性的动态Job shop调度问题启发式算法[J].机械工程学报,2006,42(8)：50-55.
[79]Leung J, Li H B, Pinedo M, et al. Minimizing total weighted completion time when scheduling orders in a flexible environment with uniform machines[J]. Information Processing Letters,2007,103(3):119-129.
[80]Vinod V, Sridharan R. Dynamic job-shop scheduling with sequence-dependent setup times simulation modeling and analysis[J]. International Journal of Advanced Manufacturing Technology,2008,36(3-4):355-372.
[81]Natarajan K, Mohanasundaran K M, Babu B S, Suresh S, et al. Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flow time and tardiness[J]. International Journal of Advanced Manufacturing Technology,2007,31(7-8):751-761.
[82]Masahiko A. An artificial neural net work optimized by a genetic algorithm for real time Flow shop scheduling[A]. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies,2000:329-332.
[83]Michecel J S. A neural net approach to real time Flow shop sequencing[J]. Computer & Industrial Engineering,2000,38(2):125-147.
[84]李树刚,吴智铭,庞小红.Job-shop的实时调度[J].上海交通大学学报,2004,38(4)：559-563.
[85]杨宏兵,严洪森.知识化制造系统中动态调度的自适应策略研究[J].控制与决策,2007,22(12)：1335-1346.
[86]Adams J, Balas E, Zawack D. The shifting bottleneck procedure for job shop scheduling[J]. Management Science,1988,34(3):391-401.
[87]Applegate D, Cook W. A computational study of job shop scheduling[J]. ORSA Journal of Computing,1991,3(2):149-156.
[88]Dauzere-Peres S, Lasserre J B. A modified shifting bottleneck procedure for job shop scheduling[J]. International Journal of Production Research,1993,31(4):923-932.
[89]Balas E, Lenstra J, Vazacopoulos A. The one-machine problem with delayed precedence constraints and its use in the job shop scheduling[J]. Management Science,1995,41(1): 94-109.
[90]Ramudhin A, Philip M. The generalized shifting bottleneck procedure[J]. European Journal of Operational Research,1996,93(1):34-48.
[91]Pinedo M, Singer M. A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop[J]. Naval Research and Logistics 1999,46:1-17.
[92]王凌.车间调度及其遗传算法[M].北京：清华大学出版社,2003.
[93]Jawahar N, Aravindan P, Ponnambalam S G. A Genetic Algorithm for Scheduling Flexible Manufacturing Systems [J]. International Journal of Advanced Manufacturing Technology, 1998,14(8):588-607.
[94]Bierwirth C, Mattfeld D C. Production scheduling and rescheduling with genetic algorithms[J]. Evolutionary Computation,1999,7(1):1-17.
[95]姜思杰,徐晓飞,李全龙.基于遗传优化算法求解作业车间调度问题[J].计算机集成制造系统,2002,8(3)：229-232.
[96]张超勇,饶运清,李培根,等.柔性作业车间调度问题的两级遗传[J].机械工程学报,2007,43(4)：119-124.
[97]柴永生,孙树栋,余建军,吴秀丽.基于免疫遗传算法的车间动态调度[J].机械工程学报,2005,41(10)：24-27.
[98]吴秀丽,孙树栋,郝京辉,等.面向成本的车间调度优化模型研究[J].机械科学与技术,2006,25(4)：421-425.
[99]宋扬,张智海,郑力.蚁群算法求解独立到达时间单机提前/拖期调度问题[J].清华大学学报(自然科学版),2005,45(11)：1577-1580.
[100]郜庆路,罗欣,杨叔子.基于蚂蚁算法的混流车间动态调度研究[J].计算机集成制造系统,2003,9(6)：456-475.
[101]Liu Y, Ye C, Chen B. Improved particle swarm optimization for flexible job-shop scheduling problem[J]. Journal of Computational Information Systems,2005,1(4): 693-699.
[102]林中伟,谢强,丁秋林,宁福臣.基于遗传算法的多级有限能力生产计划[J].小型微型计算机系统,2003,24(1)：123-125.
[103]周泓,谭小卫.一种两层生产计划问题建模及其遗传算法设计[J].系统仿真学报,2007,19(6)：3643-3649.
[104]孙明贵,李志远,金梅,李焱.库存物流管理[M].北京：中国社会科学出版社,2005.
[105]Sven Axsater. Inventory control[M]. 北京：清华大学出版社，2007.
[106]周永务,王圣东.库存控制理论与方法[M].北京：科学出版社,2009.
[107]Blair J, Berkley. A review of the kanban production control research literature[J]. Production and Operations Management,1992,1(4):393-411.
[108]Sendil Kumar C, Panneerselvam R. Literature review of JIT-KANBAN system[J]. International Journal of Advanced Manufacturing Technology,2007,32(3-4):393-408.
[109]曹振新,朱云龙.混流轿车总装配线上物料配送的研究与实践[J].计算机集成制造系统,2006,12(2)：285-291.
[110]Hung-da Wan, F. Frank Chen. A Web-based Kanban system for job dispatching, tracking, and performance monitoring[J]. International Journal of Advanced Manufacturing Technology,2008,38(9-10):995-1005.
[111]Towill D R. Dynamic analysis of an inventory and order based production control system[J]. International Journal of Production Research,1982,20(6):671-687.
[112]John S, Naim M M, Towill D R. Dynamic analysis of a WIP compensated decision support system[J]. International Journal of Manufacturing System Design,1994,1(1):283-297.
[113]Zhou L, Naim M M, Tang 0, Towill D R. Dynamic performance of a hybrid inventory system with a Kanban policy in remanufacturing process[J]. Omega,2006,34(6):585-598.
[114]陈淮莉,马登哲,张洁.基于APIOBPCS的供应链生产控制方法[J].机械工程学报,2005,41(10)：137-142.
[115]陈淮莉,马登哲,张洁.基于库存反馈补偿的生产控制及工程应用[J].上海交通大学学报,2005,39(9)：1396-1399.
[116]罗卫,张子刚,欧阳明德.基于DE-APIOBPCS策略的牛鞭效应和库存方差[J].中国管理科学.2005,13(2)：88-94.
[117]罗卫,张子刚,欧阳明德.基于APIOBPCS模型的定购决策引起的牛鞭效应[J].统计与决策,2005,164(4)：34-36.
[118]Berkley B J. A simulation study of container size in two-card kanban system[J]. International Journal of Production Research,1996,34(12):3417-3445.
[119]Salomon R. Evolutionary algorithms and gradient search:similarities and differences[J]. IEEE Transactions on Evolutionary Computation,1998,2(2):45-55.
[120]何新贵,梁久祯.利用目标函数梯度的遗传算法[J].软件学报,2001,12(7)：981-985.
[121]Peng JX, Thompson S, Li K. A gradient-guided niching method in genetic algorithm for solving continuous optimisation problems [A]. Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, China,2002:3333-3338.
[122]Yiu KFC, Liu Y, Teo KL A hybrid descent method for global optimization[J]. Journal of Global Optimization,2004,28(2):229-238.
[123]吴韫章.自动控制理论基础[M].西安：西安交通大学出版社,1999.
[124]钟秋海.现代控制理论[M].北京：高等教育出版社,2004.
[125]陆文.控制理论基础[M].北京：清华大学出版社,2008.
[126]曹德芳,周黎明.成本管理学[M].沈阳：东北大学出版社,2003.
[127]孙茂竹,姚岳.成本管理学[M].北京：中国人民大学出版社,2003.
[128]Cooper R, Kaplan R S.. How Cost Accounting Distorts Product Costs[J]. Management Accounting,1988,69(10):20-27.
[129]Cooper R, Kaplan R S. The design of cost management systems[M]. Englewood Cliffs: Prentice Hill,1991.
[130]Sunnel M. Activity Based Costing System[J]. Management Accounting,1995,76(9): 649-656.
[131]Cokins G. Activity-based Cost Management Making It Works[M]. New York:McGraw-Hill, 1997.
[132]王平心.作业成本计算理论与应用研究[M].大连：东北财经大学出版社,2001.
[133]栾庆伟.成本管理新模式[D]：(博士学位论文).大连：大连理工大学,2001.
[134]邓为民.作业成本管理系统关键技术研究[D]：(博士学位论文).西安：西北工业大学,2003.
[135]李从东,汤勇力,张洪亮,等.基于广义工作中心的作业成本法及应用[J].计算机集成制造系统,2005,11(2)：228-232.
[136]杨继良.成本会计的新方法——资源消耗会计概述[J].财会通讯,2005,(4)：22-26.
[137]白明.成本管理系统的创新资源消耗会计[J].中国管理信息化(综合版),2005,(7)：2-3.
[138]David D, Colwyn J. Throughput Accounting:Transforming Practices[J]. The British Accounting Review,1998,30(3):203-220.
[139]黄亮,王德伦,唐苏州.基于投入产出模型的制造成本计算系统研究[J].计算机集成制造系统,2007,13(11)：2133-2139.
[140]唐苏州,王德伦,黄亮.基于分层动态映射的产品成本预测方法[J].计算机集成制造系统,2010,16(2)：365-371.
[141]Cuninghame-Green R. Minimax algebra Lecture notes in economics and mathematical systems Vol.166[M]. New York:Springer-Verlag,1979.
[142]王玉荣.瓶颈管理：TOC[M].北京：机械工业出版社,2002.
[143]Lawrence S R, Buss A H. Economic analysis of production bottlenecks[J]. Mathematical Problems in Engineering,1995,1(4):341-369.
[144]Moss H K, Yu W B. Toward the estimation of bottleneck shiftiness in a manufacturing operation[J]. Production and Inventory Management Journal,1999,40(2):53-58.
[145]C. Roser, M. Nakano, M. Tanaka. Shifting bottleneck detection[A]. Proceedings of the 2002 Winter Simulation Conference, New York, USA,2002:1079-1086.
[146]Michhalewicz Z. Genetic algorithm+data structure= evolution program 3rd ed[M]. New York:Springer-Verlag,1999.
[147]Kirkpatrick S, Gelatt C D Jr, Vecchi M P. Optimization by simulated annealing[J]. Science,1983,220(4598):671-680
[148]Law A M, Kelton W D. Simulation modeling and analysis.2nd ed[M]. New York: McGraw-Hill,1991.
[149]方开泰,马长兴.正交与均匀试验设计[M].北京：科学出版社,2001.
[150]钱积新,赵均,徐祖华.预测控制[M].北京：化学工业出版社,2007.
[151]李桂华,张建华,周红.统计学[M].北京：清华大学出版社,2008.