基于遗传算法的柔性资源调度优化方法研究
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摘要
随着加工技术、自动化技术的发展,柔性制造系统和数控加工中心等带有一定柔性的生产系统逐渐出现,具有柔性资源选择的柔性车间逐渐成为企业应对动态突变市场环境和机器故障等突发事件的有力工具。互联网、云计算、物联网等新一代信息技术的迅猛发展,与制造技术相融合,孕育了一种新的先进制造模式：云制造。云制造将巨大的社会制造资源连接在一起,提供各种制造服务,柔性资源调度技术是云制造中的一项关键技术。结合课题组的相关项目,论文围绕柔性资源调度问题展开研究。首先,重点对柔性车间环境的柔性作业车间调度问题(Flexible Job Shop Scheduling Problem,FJSP)进行深入研究,然后,对云制造环境下的柔性资源服务调度问题进行初步的探讨研究。
第一章,介绍了课题的研究背景及意义。对调度问题和云制造进行了概述,综述了论文相关领域的国内外研究现状,并对现状进行了总结和分析,提出了论文的研究内容,给出了论文的章节结构。
第二章,对FJSP问题研究的总体技术框架进行了研究。首先,对FJSP问题进行了描述,给出了其数学模型；然后,确定选用遗传算法对问题进行求解,研究了基于遗传算法求解FJSP问题的总体技术框架,确定了总体研究思路,为后续各章对FJSP问题的不断深入研究提供指导。
第三章,研究了基于遗传算法优化的FJSP问题机器选择初始方法。提出一种基于短用时和设备均衡策略的遗传算法优化初始机器链方法,通过遗传算法计算产生定量优化的机器选择链群体；将上述机器选择链优化群体作为FJSP问题求解遗传算法的机器链初始群体；采用基准算例进行测试,验证了所提方法的有效性。
第四章,研究了FJSP问题基于极限调度完工时间最小化的快捷高效机器选择初始方法。初始机器选择链时,宏观上采用全局选择和局部选择分别侧重于实现对最大机器负荷和最大工件加工时间指标的优化；微观上采用随机次序取代工件工艺顺序选择工序,在考虑可选机器负荷基础上,进一步比较加工时间选择机器,兼顾最大机器负荷和最大工件加工时间指标的优化；对基准算例的测试结果验证了所提方法的有效性。
第五章,研究了FJSP问题求解遗传算法的工序链种群初始方法和基于工序编码邻域搜索机制。设计了采用主动调度、无延迟调度与启发式规则相结合的工序链群体初始方法；提出了与基于工序编码方式相结合的关键工序邻域搜索方法,避免了不可行解的产生以及染色体的检测修复等工作；提出一种基于调度甘特图的染色体标准化处理方法,对得到的标准化染色体进行邻域搜索提高了算法的搜索质量；对基准算例进行测试,验证了所提方法的有效性。
第六章,研究了FJSP问题求解遗传算法的基于空闲时间的邻域搜索机制。设计了一种基于空闲时间的邻域结构,通过查找关键工序的机器空闲时间,确定关键工序的移动位置；给出了工序移动时保证可行解的工序移动条件及证明；分析了同一机器上相邻两工序间的空闲时间,给出了最大限度查找关键工序的机器空闲时间方法；对关键工序查找对应机器所存在的空闲时间,在保证可行解条件下,将关键工序移动到空闲时间位置进行邻域搜索；采用基准算例和企业实际案例进行测试,验证了所提方法的有效性。
第七章,对云制造环境下的柔性资源服务调度进行了研究。构建了云制造环境下具有网状结构复杂产品的柔性资源服务调度优化数学模型；基于遗传算法对问题进行求解,设计了一种基于任务级别的分区编码方式；给出了多种针对分区编码可行的交叉与变异操作方式,研究了具有运输时间和任务顺序约束关系的解码方法；采用仿真算例进行测试,验证了所提方法的有效性。
第八章,对全文工作和创新点进行了总结,并对进一步的研究进行了展望。
With the development of processing technology and automation technology, production systems with a certain flexibilities including flexible manufacturing systems and CNC machining centers appeared. Flexible job shop with flexible resource selection gradually became a powerful tool to cope with dynamic market environment and sudden machine failures. Combining with manufacturing technologies, the improvements of new generation information technologies including internet, cloud computing, internet of things, and so on, gave birth to a new kind of advanced manufacturing modes:cloud manufacturing. The great social manufacturing resources were joined together to provide a variety of manufacturing services by cloud manufacturing. Flexible resource scheduling technology is a key technology in cloud manufacturing. In light of the related projects of our research group, it focused on the study of flexible resource scheduling problem in this dissertation. First, flexible job shop scheduling problem(FJSP) in the flexible workshop environment was studied in depth. Then, a preliminary study was completed on flexible resource service scheduling problem in cloud manufacturing.
In Chapter Ⅰ, it introduced the research background and significance, and gave an overview of the shop scheduling problem and cloud manufacturing. The research status of the domestic and foreign were summarized, and the limitations were analyzed. Finanlly, the main research contents and structure were presented for the dissertation.
In Chapter Ⅱ, the overall technical framework for FJSP were studied. Firstly, the FJSP problem was described, and its mathematical model was given. Then, genetic algorithm was selected for solving FJSP, and the overall technical framework based on genetic algorithm was studied. The overall research ideas were determined to provide guidance for subsequent chapters on FJSP.
In Chapter Ⅲ, optimization initialization method based on genetic algorithm of machine selection chains was studied for FJSP. A novel machine chains initialization method based on short time and workloads balancing strategies using genetic algorithm was proposed. To optimize the quality of machine chains quantitatively based on genetic algorithm. The optimized machine chains were selected and combined together as the initial machine selection population for the FJSP solving genetic algorithm. Finally, the feasibility and validity of the proposed method was demonstrated with some typical scheduling examples.
In Chapter Ⅳ, an effective machine initialization method based on the limit scheduling completion time minimization was proposed for FJSP. While initializing machine selection chains, global selection and local selection were adopted macroscopically to optimize maximum machine load and maximum job processing time respectively. Microscopically, to select the operation in random sequence instead of the job processing order, and further to compare the processing time based on considering the optional machine load for selecting machine. It took into account both of the maximum machine load and maximum job processing time optimization. Machine selection results of benchmarks were analyzed, and the effectiveness of proposed method was verified.
In Chapter V, it studied the initialization method of operations sequence population and operation-based encoding neighborhood search mechanism of genetic algorithm for FJSP. Initialization method of operations sequence was designed by combining active scheduling, non-delay scheduling with heuristic rules. Neighborhood search moving of key operations was carried out based on operation-based encoding to avoid infeasible solutions and chromosome testing-repair work. A method for chromosome standardization based on the active decoded gantt chart was proposed. Neighborhood search was implemented on the standardized chromosome individuals. Finally, benchmarks were applied to test and verify the effectiveness of the proposed algorithm.
In Chapter VI, a kind of neighborhood search genetic algorithm for FJSP based on idle time was proposed. Idle-time-based neighborhood structure was designed. Shift positions of critical operations were identified by finding the machine idle time for critical operations. To ensure the feasible solution, relative position shift conditions of the operation were given and proved. The idle time between two adjacent operations on the same machine was analyzed, and the method of maximum finding critical operation's machine idle time was provided. To find machine idle time both in front and behind of the critical operation. On condition that guaranteeing feasible solution, neighborhood search was achieved by shifting the critical operation to the idle time. The idle-time-based neighborhood search was implanted in genetic algorithm. Finally, the proposed algorithm was tested on benchmark examples and enterprise actual case, and its effectiveness was verified.
In Chapter VII, a preliminary study was completed on flexible resource service scheduling problem in cloud manufacturing. Flexible services scheduling optimization model for complex products with network structure in cloud manufacturing was built. Genetic algorithm was adopted to solve the problem. Task-rank-based zoning encoding method was designed. Several feasible crossover and mutation methods were provided for zoning coding. Decoding method with transportation time and task order constraints were studied. The feasibility and effectiveness of the algorithm were verified by simulation example. It summarized the dissertation and prospected the future research work in Chapter VIII.
第一章,介绍了课题的研究背景及意义。对调度问题和云制造进行了概述,综述了论文相关领域的国内外研究现状,并对现状进行了总结和分析,提出了论文的研究内容,给出了论文的章节结构。
第二章,对FJSP问题研究的总体技术框架进行了研究。首先,对FJSP问题进行了描述,给出了其数学模型；然后,确定选用遗传算法对问题进行求解,研究了基于遗传算法求解FJSP问题的总体技术框架,确定了总体研究思路,为后续各章对FJSP问题的不断深入研究提供指导。
第三章,研究了基于遗传算法优化的FJSP问题机器选择初始方法。提出一种基于短用时和设备均衡策略的遗传算法优化初始机器链方法,通过遗传算法计算产生定量优化的机器选择链群体；将上述机器选择链优化群体作为FJSP问题求解遗传算法的机器链初始群体；采用基准算例进行测试,验证了所提方法的有效性。
第四章,研究了FJSP问题基于极限调度完工时间最小化的快捷高效机器选择初始方法。初始机器选择链时,宏观上采用全局选择和局部选择分别侧重于实现对最大机器负荷和最大工件加工时间指标的优化；微观上采用随机次序取代工件工艺顺序选择工序,在考虑可选机器负荷基础上,进一步比较加工时间选择机器,兼顾最大机器负荷和最大工件加工时间指标的优化；对基准算例的测试结果验证了所提方法的有效性。
第五章,研究了FJSP问题求解遗传算法的工序链种群初始方法和基于工序编码邻域搜索机制。设计了采用主动调度、无延迟调度与启发式规则相结合的工序链群体初始方法；提出了与基于工序编码方式相结合的关键工序邻域搜索方法,避免了不可行解的产生以及染色体的检测修复等工作；提出一种基于调度甘特图的染色体标准化处理方法,对得到的标准化染色体进行邻域搜索提高了算法的搜索质量；对基准算例进行测试,验证了所提方法的有效性。
第六章,研究了FJSP问题求解遗传算法的基于空闲时间的邻域搜索机制。设计了一种基于空闲时间的邻域结构,通过查找关键工序的机器空闲时间,确定关键工序的移动位置；给出了工序移动时保证可行解的工序移动条件及证明；分析了同一机器上相邻两工序间的空闲时间,给出了最大限度查找关键工序的机器空闲时间方法；对关键工序查找对应机器所存在的空闲时间,在保证可行解条件下,将关键工序移动到空闲时间位置进行邻域搜索；采用基准算例和企业实际案例进行测试,验证了所提方法的有效性。
第七章,对云制造环境下的柔性资源服务调度进行了研究。构建了云制造环境下具有网状结构复杂产品的柔性资源服务调度优化数学模型；基于遗传算法对问题进行求解,设计了一种基于任务级别的分区编码方式；给出了多种针对分区编码可行的交叉与变异操作方式,研究了具有运输时间和任务顺序约束关系的解码方法；采用仿真算例进行测试,验证了所提方法的有效性。
第八章,对全文工作和创新点进行了总结,并对进一步的研究进行了展望。
With the development of processing technology and automation technology, production systems with a certain flexibilities including flexible manufacturing systems and CNC machining centers appeared. Flexible job shop with flexible resource selection gradually became a powerful tool to cope with dynamic market environment and sudden machine failures. Combining with manufacturing technologies, the improvements of new generation information technologies including internet, cloud computing, internet of things, and so on, gave birth to a new kind of advanced manufacturing modes:cloud manufacturing. The great social manufacturing resources were joined together to provide a variety of manufacturing services by cloud manufacturing. Flexible resource scheduling technology is a key technology in cloud manufacturing. In light of the related projects of our research group, it focused on the study of flexible resource scheduling problem in this dissertation. First, flexible job shop scheduling problem(FJSP) in the flexible workshop environment was studied in depth. Then, a preliminary study was completed on flexible resource service scheduling problem in cloud manufacturing.
In Chapter Ⅰ, it introduced the research background and significance, and gave an overview of the shop scheduling problem and cloud manufacturing. The research status of the domestic and foreign were summarized, and the limitations were analyzed. Finanlly, the main research contents and structure were presented for the dissertation.
In Chapter Ⅱ, the overall technical framework for FJSP were studied. Firstly, the FJSP problem was described, and its mathematical model was given. Then, genetic algorithm was selected for solving FJSP, and the overall technical framework based on genetic algorithm was studied. The overall research ideas were determined to provide guidance for subsequent chapters on FJSP.
In Chapter Ⅲ, optimization initialization method based on genetic algorithm of machine selection chains was studied for FJSP. A novel machine chains initialization method based on short time and workloads balancing strategies using genetic algorithm was proposed. To optimize the quality of machine chains quantitatively based on genetic algorithm. The optimized machine chains were selected and combined together as the initial machine selection population for the FJSP solving genetic algorithm. Finally, the feasibility and validity of the proposed method was demonstrated with some typical scheduling examples.
In Chapter Ⅳ, an effective machine initialization method based on the limit scheduling completion time minimization was proposed for FJSP. While initializing machine selection chains, global selection and local selection were adopted macroscopically to optimize maximum machine load and maximum job processing time respectively. Microscopically, to select the operation in random sequence instead of the job processing order, and further to compare the processing time based on considering the optional machine load for selecting machine. It took into account both of the maximum machine load and maximum job processing time optimization. Machine selection results of benchmarks were analyzed, and the effectiveness of proposed method was verified.
In Chapter V, it studied the initialization method of operations sequence population and operation-based encoding neighborhood search mechanism of genetic algorithm for FJSP. Initialization method of operations sequence was designed by combining active scheduling, non-delay scheduling with heuristic rules. Neighborhood search moving of key operations was carried out based on operation-based encoding to avoid infeasible solutions and chromosome testing-repair work. A method for chromosome standardization based on the active decoded gantt chart was proposed. Neighborhood search was implemented on the standardized chromosome individuals. Finally, benchmarks were applied to test and verify the effectiveness of the proposed algorithm.
In Chapter VI, a kind of neighborhood search genetic algorithm for FJSP based on idle time was proposed. Idle-time-based neighborhood structure was designed. Shift positions of critical operations were identified by finding the machine idle time for critical operations. To ensure the feasible solution, relative position shift conditions of the operation were given and proved. The idle time between two adjacent operations on the same machine was analyzed, and the method of maximum finding critical operation's machine idle time was provided. To find machine idle time both in front and behind of the critical operation. On condition that guaranteeing feasible solution, neighborhood search was achieved by shifting the critical operation to the idle time. The idle-time-based neighborhood search was implanted in genetic algorithm. Finally, the proposed algorithm was tested on benchmark examples and enterprise actual case, and its effectiveness was verified.
In Chapter VII, a preliminary study was completed on flexible resource service scheduling problem in cloud manufacturing. Flexible services scheduling optimization model for complex products with network structure in cloud manufacturing was built. Genetic algorithm was adopted to solve the problem. Task-rank-based zoning encoding method was designed. Several feasible crossover and mutation methods were provided for zoning coding. Decoding method with transportation time and task order constraints were studied. The feasibility and effectiveness of the algorithm were verified by simulation example. It summarized the dissertation and prospected the future research work in Chapter VIII.
引文
[Adams,1988] Adams J, Balas E, Zawack D. The shifting bottleneck procedure for job shop scheduling[J]. Management Science,1988,34(3):391-401.
[Amiri,2010] Amiri M, Zandieh M, Yazdani M, et al. A variable neighbourhood search algorithm for the flexible job-shop scheduling problem [J]. International Journal of Production Research,2010,48(19):5671-5689.
[Ashour,1967] Ashour S. A decomposition approach for the machine scheduling problem[J]. International Journal of Production Research,1967,6(2):109-122.
[Ashour,1973] Ashour S, Hiremath S R. A branch-and-bound approach to the job-shop scheduling problem[J]. International Journal of Production Research,1973,11(1): 47-58.
[Bagheri,2010] Bagheri A, Zandieh M, Mahdavi I, et al. An artificial immune algorithm for the flexible job-shop scheduling problem [J]. Future Generation Computer Systems, 2010,26(4):533-541.
[Balas,1969] Balas E. Machine sequencing via disjunctive graphs:an implicit enumeration algorithm [J]. Operations Research,1969,17(6):941-957.
[Balas,1998] Balas E, Vazacopoulos A. Guided local search with shifting bottleneck for job shop scheduling[J]. Management Science,1998,44(2):262-275.
[Baykasoglu,2002] Baykasoglu A. Linguistic-based meta-heuristic optimization model for flexible job shop scheduling[J]. International Journal of Production Research,2002, 40(17):4523-4543.
[Brandimarte,1993] Brandimarte P. Routing and scheduling in a flexible job shop by tabu search[J]. Annals of Operations Research,1993,41:157-183.
[Brucker,1990] Brucker P, Schlie R. Job-shop scheduling with multi-purpose machines[J]. Computing,1990,45(4):369-375.
[Brucker,1994] Brucker P, Jurisch B, Sievers B. A branch and bound algorithm for the job-shop scheduling problem[J]. Discrete Applied Mathematics,1994,49:107-127.
[Cao,2012]Cao Y, Song X Y, Zhang Y, et al. Application of ACOTS hybrid algorithm for job shop scheduling problems[C]. Proceedings of 2012 International Conference on Modeling, Identification and Control, Wuhan, China,2012.
[Carlier,1982] Carlier J. The one-machine sequencing problem[J]. European Journal of Operational Research,1982,11(1):42-47.
[Chen,1999] Chen H X, Ihlow J, Lehmann C. A genetic algorithm for flexible job-shop scheduling[C]. Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit, Michigan,1999.
[Cheng,1996] Cheng R W, Gen M, Tsujimura Y. A tutorial survey of job-shop scheduling problems using genetic algorithms-I. representation[J]. Computers & Industrial Engineering,1996,30(4):983-997.
[Cheng,1999] Cheng R W, Gen M, Tsujimura Y. A tutorial survey of job-shop scheduling problems using genetic algorithms, part II:hybrid genetic search strategies[J]. Computers & Industrial Engineering,1999,36:343-364.
[Chiang,2013] Chiang T C, Lin H J. A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling[J]. International Journal of Production Economics,2013,141(1):87-98.
[Chu,1992]Chu C, Portmann M C, Proth J M. A splitting-up approach to simplify job-shop scheduling problems[J]. International Journal of Production Research,1992,30(4): 859-870.
[Collinot,1989] Collinot A, Le Pape C. Controlling the behavior of knowledge sources within sonia[C]. Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences,1989,3:221-228.
[Collinot,1991] Collinot A, Le Pape C. Adapting the behavior of a job-shop scheduling system [J]. Decision Support Systems,1991,7(4):341-353.
[Dauzere-peres,1993] 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.
[Davis,1985] Davis L. Job-shop scheduling with genetic algorithms[C]. Proceedings of the 1st International Conference on Genetic Algorithms,1985.
[Demirkol,1997] Demirkol E, Mehta S, Uzsoy R. A computational study of shifting bottleneck procedures for shop scheduling problems[J]. Journal of Heuristics,1997,3:111-137.
[Dorigo,1996] Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics,1996, 26(1):29-41.
[Dorigo,1997] Dorigo M, Gambardella L M. Ant colony system:a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[Fernandes,2007] Fernandes S, Lourenco H R. A GRASP and branch-and-bound metaheuristic for the job-shop scheduling[J]. Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science,2007,4446:60-71.
[Fisher,1973] Fisher M L. Optimal solution of scheduling problems using lagrange multipliers:Part I[J]. Operations Research,1973,21:1114-1127.
[Fox,1983]Fox M S, Allen B P, Smith S F, et al. ISIS:a constraint-directed reasoning approach to job shop scheduling[C]. Proceedings of IEEE Conference on Trends and Applications, Gaithersburg, Maryland,1983.
[Fox,1984]Fox M S, Smith S F. ISIS-a knowledge-based system for factory scheduling[J]. Expert Systems,1984,1(1):25-49.
[Gao,2007]Gao J, Gen M, Sun L Y, et al. A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems[J]. Computers & Industrial Engineering,2007,53(1):149-162.
[Gao,2008]Gao J, Sun L Y, Gen M. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems[J]. Computers & Operations Research,2008,35(9):2892-2907.
[Gao,2011]Gao L, Zhang G H, Zhang L P, et al. An efficient memetic algorithm for solving the job shop scheduling problem[J]. Computers & Industrial Engineering,2011,60(4): 699-705.
[Ge,2008]Ge H W, Sun L, Liang Y C, et al. An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans,2008,38(2):358-368.
[Gholami,2009] Gholami M, Zandieh M. Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop[J]. Journal of Intelligent Manufacturing,2009, 20(4):481-498.
[Giffler,1960] Giffler B, Thompson G L. Algorithms for solving production-scheduling problems[J]. Operations Research,1960,8(4):487-503.
[Glover,1986] Glover F. Future paths for integer programming and links to artificial intelligence[J]. Computers & Operations Research,1986,13(5):533-549.
[Glover,1989] Glover F. Tabu search-part I[J]. ORSA Journal on Computing,1989,1(3): 190-206.
[Glover,1990] Glover F. Tabu search-part II[J]. ORSA Journal on Computing,1990,2(1): 4-32.
[Glover,1997] Glover F, Laguna M. Tabu search[M]. Dordrecht:Kluwer Academic Publishers,1997.
[Goncalves,2005] Goncalves J F, Magalhaes Mendes J J, Resende M G C. A hybrid genetic algorithm for the job shop scheduling problem[J]. European Journal of Operational Research,2005,167(1):77-95.
[Grabowski,2005] Grabowski J, Wodecki M. A very fast tabu search algorithm for job shop problem[C]. Rego C, Alidaee B. Metaheuristic Optimization via Memory and Evolution:Tabu Search and Scatter Search. Boston:Kluwer Academic Publishers, 2005:117-144.
[Graves,1981] Graves S C. A review of production scheduling[J]. Operations Research,1981, 29(4):646-675.
[Gu,2012]Gu W B, Tang D B, Zheng K. Minimizing makespan in job-shop scheduling problem using an improved adaptive particle swarm optimization algorithm[C]. In: 2012 24th Chinese Control and Decision Conference(CCDC),2012:3189-3193.
[Guimaraes,2006] Guimaraes K F, Fernandes M A. An approach for flexible job-shop scheduling with separable sequence-dependent setup time[C]. IEEE International Conference on Systems, Man and Cybernetics. Taipei,8-11 Oct 2006,5:3727-3731.
[Guo,2010]Guo H, Zhang L, Tao F, et al. Research on the measurement method of flexibility of resource service composition in cloud manufacturing[J]. Advanced Materials Research,2010,139-141:1451-1454.
[Gutowski,2005] Gutowski T, Murphy C, Allen D, et al. Environmentally benign manufacturing observations from Japan, Europe and the United States[J]. Journal of Cleaner Production,2005,13:1-17.
[Hansen,1986] Hansen P. The steepest ascent mildest descent heuristic for combinatorial programming[C]. Congress on Numerical Methods in Combinatorial Optimization, Capri, Italy,1986.
[Haupt,1989] Haupt R. A survey of priority rule-based scheduling[J]. OR Spektrum,1989, 11:3-16.
[Heinonen,2007] Heinonen J, Pettersson F. Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem[J]. Applied Mathematics and Computation,2007,187:989-998.
[Hmida,2010] Hmida A B, Haouari M, Huguet M, et al. Discrepancy search for the flexible job shop scheduling problem[J]. Computers & Operations Research,2010,37(12): 2192-2201.
[Ho,2005]Ho N, Tay J. Evolving dispatching rules for solving the flexible job-shop problem[C]. The 2005 IEEE Congress on Evolutionary Computation.2-5 Sept 2005,3: 2848-2855.
[Holland,1975] Holland J H. Adaptation in natural and artificial systems[M]. Ann Arbor:The University of Michigan Press,1975.
[Holthaus,1997] Holthaus O, Rajendran C. Efficient dispatching rules for scheduling in a job shop[J]. International Journal of Production Economics,1997,48:87-105.
[Hou,1994]Hou E S H, Ansari N, Ren H. A genetic algorithm for multiprocessor scheduling[J]. IEEE Transactions on Parallel and Distributed Systems,1994,5(2): 113-120.
[Huang,2004] Huang W Q, Yin A H. An improved shifting bottleneck procedure for the job shop scheduling problem[J]. Computers & Operations Research,2004,31:2093-2110.
[Huang,2008] Huang K L, Liao C J. Ant colony optimization combined with taboo search for the job shop scheduling problem[J]. Computers & Operations Research,2008,35: 1030-1046.
[Hurink,1994] Hurink J, Jurisch B, Thole M. Tabu search for the job-shop scheduling problem with multi-purpose machines[J]. Operations-Research-Spektrum,1994,15(4):205-215.
[Imanipour,2006] Imanipour N. Modeling & solving flexible job shop problem with sequence dependent setup times[C]. International Conference on Service Systems and Service Management. Troyes,25-27 Oct 2006,2:1205-1210.
[Jamili,2011] Jamili A, Shafia M A, Tavakkoli-moghaddam R. A hybridization of simulated annealing and electromagnetism-like mechanism for a periodic job shop scheduling problem[J]. Expert Systems with Applications,2011,38(5):5895-5901.
[Jansen,2000] Jansen K, Mastrolilli M, Solis-oba R. Approximation algorithms for flexible job shop problems[J]. LATIN 2000:Theoretical Informatics Lecture Notes in Computer Science.2000,1776:68-77.
[Johnson,1954] Johnson S M. Optimal two-and three-stage production schedules with setup times included[J]. Naval Research Logistics Quarterly,1954,1(1):61-68.
[Kacem,2001] Kacem I, Hammadi S, Borne P. Approach by localization and genetic manipulation algorthm for flexible job-shop scheduling problem[C]. IEEE International Conference on Systems, Man, and Cybernetics. Tucson, AZ,07-10 Oct 2001,4: 2599-2604.
[Kacem,2002] Kacem I, Hammadi S, Borne P. Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems[J]. IEEE Transactions on Systems, Man and Cybernetics, Part C:Applications and Reviews, 2002,32(1):1-13.
[Karimi,2012] Karimi H, Rahmati S H A, Zandieh M. An efficient knowledge-based algorithm for the flexible job shop scheduling problem[J]. Knowledge-Based Systems, 2012,36:236-244.
[Kennedy,1995] Kennedy J, Eberhart R. Particle swarm optimization[C]. IEEE International Conference on Neural Networks,1995,4:1942-1948.
[Kirkpatrick,1983] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science, New Series,1983,220(4598):671-680.
[Kruger,1995] Kruger K, Shakhlevich N V, Sotskov Y N, et al. A heuristic decomposition algorithm for scheduling problems on mixed graphs[J]. Journal of the Operational Research Society,1995,46(12):1481-1497.
[Lei,2008]Lei D M. A Pareto archive particle swarm optimization for multi-objective job shop scheduling[J]. Computers & Industrial Engineering,2008,54:960-971.
[Li,2011a]Li J Q, Pan QK, Suganthan P N, et al. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem[J]. International Journal of Advanced Manufacturing Technology,2011,52(5-8):683-697.
[Li,2011b]Li J, Pan Q, Xie S, et al. A hybrid artificial bee colony algorithm for flexible job shop scheduling problems[J]. Int. J. of Computers Communications & Control,2011, (2):286-296.
[Li,2012a]Li J Q, Pan Q K. Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity[J]. Applied Soft Computing,2012,12(9): 2896-2912.
[Li,2012b]Li J Q, Xie S X, Sun T, et al. Solving fuzzy job-shop scheduling problem by genetic algorithm[C]. Control and Decision Conference, Chinese,2012.
[Lin,2010]Lin T L, Horng S J, Kao T W, et al. An efficient job-shop scheduling algorithm based on particle swarm optimization[J]. Expert Systems with Applications,2010,37: 2629-2636.
[Lobato,2012] Lobato F S, Gedraite R, Neiro S M S. Solution of flow shop scheduling problems using the differential evolution algorithm[C]. IN:EngOpt 2012-3rd, International Conference on Engineering Optimization, Rio de Janeiro, Brazil,2012.
[Luh,2000]Luh P B, Zhao X, Wang Y J, et al. Lagrangian relaxation neural networks for job shop scheduling[J]. IEEE Transactions on Robotics and Automation,2000,16(1): 78-88.
[Manne,1960] Manne A S. On the job-shop scheduling problem[J]. Operations Research, 1960,8:219-223.
[Mastrolilli,2000] Mastrolilli M, Gambardella L M. Effective neighborhood functions for the flexible job shop problem[J]. Journal of scheduling,2000,3(1):3-20.
[Mati,2001] Mati Y, Rezg N, Xie X L. An integrated greedy heuristic for a flexible job shop scheduling problem[C]. IEEE International Conference on Systems, Man, and Cybernetics. Tucson, AZ,07-10 Oct 2001,4:2534-2539.
[Matsuo,1988] Matsuo H, Suh C J, Sullivan R S. A controlled search simulated annealing method for the general job-shop scheduling problem[R]. Working Paper,03-04-88, Graduate School of Business, University of Texas at Austin, Austin, Texas, USA, 1988.
[Melab,2012] Melab N, Chakroun I, Mezmaz M, et al. A GPU-accelerated branch-and-bound algorithm for the flow-shop scheduling problem[C].2012 IEEE International Conference on Cluster Computing, France,2012.
[Metropolis,1953] Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equation of state calculations by fast computing machines[J]. Journal of Chemical Physics,1953,21(6): 1087-1092.
[Mohammad,2007] Mohammad S M, Parviz F. Flexible job shop scheduling by tabu search algorithms[J]. International Journal of Advanced Manufacturing Technology,2007,32: 563-570.
[Mohammad,2011] Mohammad R, Raeesi N, Ziad K. A machine operation lists based memetic algorithm for job shop scheduling[C]. IEEE Congress on Evolutionary Computation(CEC), New Orleans, LA, USA,2011:2436-2443.
[Mohammad,2012] Mohammad R, Raeesi N, Ziad K. A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic[J]. Memetic Computing, 2012,4(3):231-245.
[Moslehi,2011] Moslehi G, Mahnam M. A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search[J]. International Journal of Production Economics,2011,129:14-22.
[Naderi,2010] Naderi B, Tavakkoli-moghaddam R, Khalili M. Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan[J]. Knowledge-Based Systems, 2010,23:77-85.
[Najid,2002] Najid N M, Dauzere-peres S, Zaidat A. A modified simulated annealing method for flexible job shop scheduling problem [C].2002 IEEE International Conference on Systems, Man and Cybernetics,2002.
[Neifar,2006] Neifar F, Gzara M, Moukrim A, et al. Hybrid evolutionary algorithm with insertion heuristics for the flexible job shop problem[C]. International Conference on Service Systems and Service Management. Troyes,25-27 Oct 2006,2:1211-1216.
[Nowicki,1996] Nowicki E, Smutnicki C. A fast taboo search algorithm for the job shop problem[J]. Management Science,1996,42(6):797-813.
[Nowicki,2002] Nowicki E, Smutnicki C. Some new tools to solve the job-shop problem [R]. Technical Report 60/02, Institute of Engineering Cybernetics, Wroclaw University of Technology,2002.
[Ong,2005]Ong Z X, Tay J C, Kwoh C K. Applying the clonal selection principle to find flexible job-shop schedules[J]. Artificial Immune Systems:Lecture Notes in Computer Science.2005,3627:442-455.
[Pacino,2011] Pacino D, Hentenryck P V. Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling[C]. Proceedings of the Twenty-Second international joint conference on Artificial Intelligence.2011,3: 1997-2002.
[Panwalkar,1977] Panwalkar S S, Iskander W. A survey of scheduling rules[J]. Operations Research,1977,25(1):45-61.
[Park,2003] Park B J, Choi H R, Kim H S. A hybrid genetic algorithm for the job shop scheduling problems[J]. Computers & Industrial Engineering,2003,45(4):597-613.
[Paulli,1995] Paulli J. A hierarchical approach for the FMS scheduling problem[J]. European Journal of Operational Research,1995,86(1-5):32-42.
[Pesch,1996] Pesch E, Tetzlaff U A W. Constraint propagation based scheduling of job shops[J]. Journal on Computing,1996,8(2):144-157.
[Pezzella,2000] Pezzella F, Merelli E. A tabu search method guided by shifting bottleneck for the job shop scheduling problem[J]. European Journal of Operational Research,2000, 120:297-310.
[Pezzella,2008] Pezzella F, Morganti G, Ciaschetti G. A genetic algorithm for the flexible job-shop scheduling problem[J]. Computers & Operations Research,2008,35: 3202-3212.
[Pinedo,2007] Pinedo M调度：原理、算法和系统[M].北京：清华大学出版社,2007.
[Ponsich,2013] Ponsich A, Coello Coello C A. A hybrid differential evolution-tabu search algorithm for the solution of job-shop scheduling problems[J]. Applied Soft Computing,2013,13(1):462-474.
[Priore,2003] Priore P, Fuente D, Pino R, et al. Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning[J]. Integrated Manufacturing Systems,2003,14(2):160-168.
[Qing-dao-er-ji,2012] Qing-dao-er-ji R, Wang Y P. A new hybrid genetic algorithm for job shop scheduling problem[J]. Computers & Operations Research,2012,39:2291-2299.
[Rahmati,2012] Rahmati S H A, Zandieh M. A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem[J]. International Journal of Advanced Manufacturing Technology,2012,58(9-12):1115-1129.
[Rego,2009] Rego C, Duarte R. A filter-and-fan approach to the job shop scheduling problem[J]. European Journal of Operational Research,2009,194(3):650-662.
[Rossi,2007] Rossi A, Dini G. Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method[J]. Robotics and Computer-Integrated Manufacturing,2007,23(5):503-516.
[Saidi-mehrabad,2007] Saidi-mehrabad M, Fattahi P. Flexible job shop scheduling with tabu search algorithms[J]. International Journal of Advanced Manufacturing Technology, 2007,32(5-6):563-570.
[Santosa,2012] Santosa B, Nurkhalida L. A cross entropy-genetic algorithm approach for multi objective job shop scheduling problem[C]. Proceedings of the 2012 International Conference on Industrial ngineering and Operations Management, Istanbul, Turkey, 2012.
[Scrich,2004] Scrich C R, Armentano V A, Laguna M. Tardiness minimization in a flexible job shop:a tabu search approach[J]. Journal of Intelligent Manufacturing,2004,15(1): 103-115.
[Sha,2006]Sha D Y, Hsu C Y. A hybrid particle swarm optimization for job shop scheduling problem[J]. Computers & Industrial Engineering,2006,51:791-808.
[Stora,1995] Storn R, Price K. Differential evolution:a simple and efficient adaptive scheme for global optimization over continuous spaces[R]. Berkeley, International Computer Science Institute,1995.
[Subramaniam,2000] Subramaniam V, Ramesh T, Lee G K, et al. Job shop scheduling with dynamic fuzzy selection of dispatching rules[J]. International Journal of Advanced Manufacturing Technology,2000,16:759-764.
[Taillard,1989] Taillard E D. Parallel taboo search technique for the jobshop scheduling problem[R]. Technical Report ORWP 89/11, Departement de mathematiques, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland,1989.
[Tamaki,2001] Tamaki H, Ono T, Murao H, et al. Modeling and genetic solution of a class of flexible job shop scheduling problems[C]. International Conference on Emerging Technologies and Factory Automation. Antibes-Juan les Pins, France,15-18 Oct 2001, 2:343-350.
[Tang,2011] Tang J C, Zhang G J, Lin B B, et al. A hybrid algorithm for flexible job-shop scheduling problem[J]. Procedia Engineering,2011,15:3678-3683.
[Teekeng,2011] Teekeng W, Thammano A. A combination of shuffled frog leaping and fuzzy logic for flexible job-shop scheduling problems[J]. Procedia Computer Science,2011, 6:69-75.
[Thamilselvan,2012] Thamilselvan R, Balasubramanie P. Integration of genetic algorithm with tabu search for job shop scheduling with unordered subsequence exchange crossover[J]. Journal of Computer Science,2012,8(5):681-693.
[Van,1988]Van Laarhoven P J M, Aarts E H L, Lenstra J K. Job-shop scheduling by simulated annealing[R]. Report OSR8809, Centrum voor Wiskunde en Informatica, Amsterdam, Netherlands,1988.
[Van,1991]Van Hulle M M. A goal programming network for mixed integer linear programming:a case study for the job-shop scheduling problem [J]. International Journal of Neural Systems,1991,2(3):201-209.
[Van,1992]Van Laarhoven P J M, Aarts E H L, Lenstra J K. Job shop scheduling by simulated annealing[J]. Operations Research,1992,40(1):113-125.
[Vilcot,2006] Vilcot G, Billaut J, Esswein C. A genetic algorithm for a bicriteria flexible job shop scheduling problem[C]. International Conference on Service Systems and Service Management. Troyes, Oct 2006,2:1240-1244.
[Wang,2008] Wang S J, Zhou B H, Xi L F. A filtered-beam-search-based heuristic algorithm for flexible job-shop scheduling problem[J]. International Journal of Production Research,2008,46(11):3027-3058.
[Wang,2011] Wang L, Tang D B. An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem[J]. Expert Systems with Applications,2011,38(6):7243-7250.
[Wang,2012a] Wang L, Wang S Y, Xu Y, et al. A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem[J]. Computers & Industrial Engineering,2012,62(4):917-926.
[Wang,2012b] Wang L, Zhou G, Xu Y, et al. An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling[J]. International Journal of Advanced Manufacturing Technology,2012,60(9-12):1111-1123.
[Watanabe,2005] Watanabe M, Ida K, Gen M. A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem[J]. Computers & Industrial Engineering,2005,48(4):743-752.
[Wu,2005]Wu Z B, Weng M X. Multiagent scheduling method with earliness and tardiness objectives in flexible job shops[J]. IEEE Transactions on Systems, Man, and Cybernetics-part b:cybernetics,2005,35(2):293-301.
[Xia,2005]Xia W J, Wu Z M. An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems[J]. Computers & Industrial Engineering,2005, 48:409-425.
[Xing,2010] Xing L N, Chen Y W, Wang P, et al. A knowledge-based ant colony optimization for flexible job shop scheduling problems[J]. Applied Soft Computing,2010,10: 888-896.
[Xu,2012]Xu Y, Wang L, Wang S Y, et al. An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines[J]. Engineering Optimization,2013,45(12):1409-1430.
[Zhang,2005] Zhang H P, Gen M. Multistage-based genetic algorithm for flexible job-shop scheduling problem[J]. Journal of Complexity Interna-tional,2005,11:223-232.
[Zhang,2007] Zhang C Y, Li P G, Guan Z L, et al. A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem[J]. Computers & Operations Research,2007,34:3229-3242.
[Zhang,2008a] Zhang C Y, Rao Y Q, Li P G. An effective hybrid genetic algorithm for the job shop scheduling problem [J]. International Journal of Advanced Manufacturing Technology,2008,39:965-974.
[Zhang,2008b] Zhang G H, Shi Y, Gao L. A genetic algorithm and tabu search for solving flexible job shop schedules[C]. International Symposium on Computational Intelligence and Design,2008.
[Zhang,2009] Zhang G H, Shao X Y, Li P G, et al. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J]. Computers & Industrial Engineering,2009,56(4):1309-1318.
[Zhang,2010] Zhang L, Guo H, Tao F, et al. Flexible management of resource service composition in cloud manufacturing[C].2010 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao,7-10 Dec 2010: 2278-2282.
[Zhang,2011] Zhang G H, Gao L, Shi Y. An effective genetic algorithm for the flexible job-shop scheduling problem[J]. Expert Systems with Applications,2011,38(4): 3563-3573.
[Zhang,2012] Zhang Q, Manier H, Manier M A. A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times[J]. Computers & Operations Research,2012,39(7):1713-1723.
[Zhao,2001] Zhao C W, Wu ZM.A genetic algorithm approach to the scheduling of fmss with multiple routes[J]. International Journal of Flexible Manufacturing Systems, 2001,13(1):71-88.
[Zhou,2001] Zhou H, Feng Y C, Han L M. The hybrid heuristic genetic algorithm for job shop scheduling[J]. Computers & Industrial Engineering,2001,40:191-200.
[Zhu,2010]Zhu K C, Jiang M Y. The optimization of job shop scheduling problem based on artificial fish swarm algorithm with tabu search strategy[C]. In:Third International Workshop on Advanced Computational Intelligence,2010,323-327.
[Zribi,2004] Zribi N, Kacem I, El Kamel A, et al. Optimization by phases for the flexible job-shop scheduling problem[C].5th Control Conference. Melbourne, Victoria, Australia,20-23 July 2004, (3):1889-1895.
[Zribi,2005] Zribi N, Borne P. Hybrid genetic algorithm for the flexible job-shop problem under maintenance constraints[J]. Advances in Natural Computation, Lecture Notes in Computer Science.2005,3612:259-268.
[Zribi,2006] Zribi N, El Kamel A, Borne P. Total tardiness in a flexible job-shop[C]. IMACS Multiconference on Computational Engineering in Systems Applications. Beijing, China,4-6 Oct 2006:1543-1549.
[Zribi,2007] Zribi N, Kacem I, El Kamel A, et al. Assignment and scheduling in flexible job-shops by hierarchical optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews,2007,7(4):652-661.
[Zuo,2012]Zuo X Q, Wang C L, Tan W. Two heads are better than one:an AIS- and TS-based hybrid strategy for job shop scheduling problems[J]. International Journal of Advanced Manufacturing Technology,2012,63(1-4):155-158.
[白俊杰,2010]白俊杰,王宁生,唐敦兵.一种求解多目标柔性作业车间调度的改进粒子群算法[J].南京航空航天大学学报,2010,42(4)：447-453.
[蔡良伟,2010]蔡良伟,李霞.基于混合蛙跳算法的作业车间调度优化[J].深圳大学学报(理工版),2010,27(4)：391-395.
[陈彩丽,2010]陈彩丽.基于遗传算法和仿真的车间调度优化系统研究与开发[D].杭州：浙江大学,2010.
[陈成,2011]陈成,邢立宁.求解柔性作业车间调度问题的遗传-蚁群算法[J].计算机集成制造系统,2011,17(3)：615-621.
[陈恩红,1998]陈恩红,薛瀚宏.基于约束满足的Job-Shop调度问题求解方法研究[J].软件学报,1998,9(12)：946-948.
[陈钢,2007]陈钢,高杰,孙林岩.带瓶颈移动法的混合遗传算法求解柔性作业车间调度[J].系统工程,2007,25(9)：91-97.
[陈荣秋,1995]陈荣秋.生产计划与控制：概念、方法与系统[M].武汉：华中理工大学出版社,1995.
[程方晓,2011]程方晓.基于自适应保持多样性遗传算法的汽车动力传动系多目标优化[D].吉林：吉林大学,2011.
[崔静静,2011]崔静静,孙延明,车兰秀.改进细菌觅食算法求解车间作业调度问题[J].计算机应用研究,2011,28(9)：3324-3326.
[方开泰,1994]方开泰.均匀设计与均匀设计表[M].北京：科学出版社,1994.
[方水良,2012]方水良,姚嫣菲,赵诗奎.柔性车间调度的改进遗传算法[J].浙江大学学报(工学版),2012,46(4)：629-635.
[高亮,2006]高亮,高海兵,周驰.基于粒子群优化的开放式车间调度[J].机械工程学报,2006,42(2)：129-134.
[谷峰,2005]谷峰,陈华平,卢冰原.病毒遗传算法在柔性工作车间调度中的应用[J].系统工程与电子技术,2005,27(11)：1953-1956.
[谷峰,2006]谷峰,陈华平,卢冰原.基于均匀设计的多目标遗传算法在柔性工作车间 调度中的应用[J].系统工程理论方法与应用,2006,15(6)：548-551.
[顾新建,2010]顾新建,张栋,纪杨建,等.制造业服务化和信息化融合技术[J].计算机集成制造系统,2010,16(11)：2530-2536.
[郭冬芬,2007]郭冬芬,李铁克.基于约束满足的车间调度算法综述[J].计算机集成制造系统,2007,13(1)： 117-125.
[何大阔,2005]何大阔,王福利,贾明兴.遗传算法初始种群与操作参数的均匀设计[J].东北大学学报：自然科学版,2005,26(9)：828-831.
[何建佳,2009]何建佳,徐福缘,叶伟.置换流水车间调度的文化量子粒子群算法[J].机械设计与制造,2009,(8)：17-19.
[何霆,2000]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.
[黄明,2009]黄明,刘鹏飞,梁旭.面向作业车间的自适应非均匀窗口蚁群算法[J].计算机集成制造系统,2009,15(10)：1973-1978.
[黄明,2012]黄明,刘鹏飞.混合生产形态下作业车间调度问题研究[M].北京：北京师范大学出版社,2012.
[黄志,2005]黄志,黄文奇.一种基于禁忌搜索方法的作业车间调度[J].华中科技大学学报(自然科学版),2005,33(12)：109-111.
[黄竹君,2009]黄竹君.基于启发式调度规则的车间作业计划算法及仿真研究[D].武汉：武汉科技大学,2009.
[纪树新,1995]纪树新.基于遗传算法的车间作业调度系统研究[D].杭州：浙江大学,1995.
[纪树新,1997]纪树新,钱积新,孙优贤.车间作业调度遗传算法中的编码研究[J].信息与控制,1997,26(5)：393-400.
[贾兆红,2008]贾兆红.粒子群优化算法在柔性作业车间调度中的应用研究[D].合肥：中国科学技术大学,2008.
[姜思杰,2003]姜思杰,张付亮,王孔茂.基于遗传和禁忌算法求解一类车间调度问题[J].计算机集成制造系统,2003,9(11)：984-988.
[李伯虎,2010]李伯虎,张霖,王时龙,等.云制造—面向服务的网络化制造新模式[J].计算机集成制造系统,2010,(1)：1-7,16.
[李春泉,2011]李春泉,尚玉玲,胡春杨.云制造的体系结构及其关键技术研究[J].组合 机床与自动化加工技术,2011,(7)：104-107,112.
[李海波,2013]李海波.云制造环境下基于工作流的多粒度资源组合方法[J].计算机集成制造系统,2013,19(1)：210-216.
[李慧芳,2012]李慧芳,董训,宋长刚.制造云服务智能搜索与匹配方法[J].计算机集成制造系统,2012,18(7)：1485-1493.
[李飒,2000]李飒,王云莉,段广洪,等.基于多智能体结构的车间调度方法[J].机械科学与技术,2000,19(6)：956-958.
[李铁克,2010]李铁克,王伟玲,张文学.基于文化遗传算法求解柔性作业车间调度问题[J].计算机集成制造系统,2010,16(4)：861-866.
[李伟平,2011]李伟平,林慧苹,莫同,等.云制造中的关键技术分析[J].制造业自动化,2011,33(1)：7-10.
[梁昌勇,2009]梁昌勇,陆青,张恩桥,等.基于均匀设计的多智能体遗传算法研究[J].系统工程学报,2009,24(1)：109-113.
[梁旭,2005a]梁旭,黄明,常征.求解车间调度问题的一种新遗传退火混合策略[J].计算机集成制造系统,2005,11(6)：851-854.
[梁旭,2005b]梁旭,黄明.禁忌-并行遗传算法在作业车间调度中的应用[J].计算机集成制造系统,2005,11(5)：678-681.
[刘爱军,2011]刘爱军.离散制造车间多生产模式下作业调度研究[D].重庆：重庆大学,2011.
[刘爱军,2012]刘爱军,杨育,邢青松,等.含精英策略的小生境遗传退火算法研究及其应用[J].中国机械工程,2012,23(5)：556-563.
[刘海霞,2008]刘海霞,李仁旺,李学,等.基于遗传模拟退火算法的制造网格资源调度策略[J].计算机工程与应用,2008,44(6)：234-236,242.
[刘民,2000]刘民,吴澄,杨英杰.并行多机调度问题的一种基于组合规则的遗传算法[J].电子学报,2000,28(5)：1-3.
[刘敏,2011]刘敏,张超勇,张国军,等.基于混合粒子群优化算法的置换流水车间调度问题研究[J].中国机械工程,2011,22(17)：2048-2053.
[刘强,2013]刘强,王磊,陈新度,等.云制造服务平台的资源使用及访问控制[J].计算机集成制造系统,2013,19(6)：1414-1422.
[刘琼,2009]刘琼,张超勇,饶运清,等.改进遗传算法解决柔性作业车间调度问题[J].工 业工程与管理,2009,14(2)：59-66.
[刘士军,2012]刘士军,曲本科,武蕾,等.自组织云制造资源聚集框架与多维属性区间搜索方法研究[J].计算机辅助设计与图形学学报,2012,24(3)：299-307.
[刘卫宁,2013]刘卫宁,刘波,孙棣华.面向多任务的制造云服务组合[J].计算机集成制造系统,2013,19(1)：199-209.
[刘志峰,2004]刘志峰,梁峰,朱华炳.基于产品结构树的CAPP系统开发技术[J].农业机械学报,2004,35(2)：92-95.
[刘志勇,2010]刘志勇,吕文阁,谢庆华,等.应用改进蚁群算法求解柔性作业车间调度问题[J].工业工程与管理,2010,15(3)：115-119.
[马川,2009]马川,王涛,王宝文,等.一种基于混沌神经网络的作业车间调度算法[J].机床与液压,2009,37(7)：11-14,20.
[孟祥旭,2011]孟祥旭,刘士军,武蕾,等.云制造模式与支撑技术[J].山东大学学报(工学版),2011,41(5)：13-20.
[潘全科,2006]潘全科,王文宏,朱剑英.一类解决车间调度问题的遗传退火算法[J].机械科学与技术,2006,25(3)：317-321.
[潘全科,2007]潘全科,王文宏,朱剑英.解决无等待流水车间调度问题的离散粒子群优化算法[J].计算机集成制造系统,2007,13(6)：1127-1130,1136.
[潘全科,2010]潘全科,王凌,高亮,等.基于差分进化与块结构邻域的作业车间调度优化[J].机械工程学报,2010,46(22)：182-188.
[庞哈利,2003]庞哈利.柔性Job shop集成化计划调度模型及其求解算法[J].控制与决策,2003,18(1)：34-39.
[彭传勇,2006]彭传勇,高亮,邵新宇,等.求解作业车间调度问题的广义粒子群优化算法[J].计算机集成制造系统,2006,12(6)：911-917,923.
[彭海波,2006]彭海波,基于Petri网的机加工车间建模与仿真研究[D].武汉：武汉科技大学,2006.
[任磊,2011]任磊,张霖,张雅彬,等.云制造资源虚拟化研究[J].计算机集成制造系统,2011,17(3)：511-518.
[任彦君,2010]任彦君,黎冰,顾幸生.改进的人工鱼群算法在置换Flow Shop调度中的应用[J].华东理工大学学报(自然科学版),2010,36(1)：93-98.
[上官春霞,2008]上官春霞,周泓,师瑞峰,等.作业车间排序重调度问题及其改进修 复约束满足算法[J].计算机集成制造系统,2008,14(9)：1742-1751,1773.
[宋强磊,2012]宋强磊,车阿大.量子进化算法在生产调度中的应用综述[J].计算机应用研究,2012,29(5)：1601-1605.
[苏子林,2008]苏子林.车间调度问题及其进化算法分析[J].机械工程学报,2008,44(8)：242-247.
[孙志峻,2001]孙志峻,朱剑英.具有柔性加工路径的作业车间智能优化调度[J].机械科学与技术,2001,20(6)：931-932,935.
[邰丽君,2013]邰丽君,胡如夫,赵韩,等.面向云制造服务的制造资源多目标动态优化调度[J].中国机械工程,2013,24(12)：1616-1622.
[谈峰,2008]谈峰,王伟.多种群差分进化算法及在柔性车间作业调度中的应用[J].湖南农业大学学报(自然科学版),2008,34(1)：105-108.
[覃朝勇,2010]覃朝勇,刘向,郑建国.求解多目标job-shop生产调度问题的量子进化算法[J].计算机应用研究,2010,27(3)：849-852.
[唐启义,2010]唐启义.DPS数据处理系统：实验设计、统计分析及数据挖掘[M].北京：科学出版社,2010.
[陶飞,2011]陶飞,张霖,郭华,等.云制造特征及云服务组合关键问题研究[J].计算机集成制造系统,2011,17(3)：477-486.
[王常青,2004]王常青,操云甫,戴国忠.用双向收敛蚁群算法解作业车间调度问题[J].计算机集成制造系统,2004,10(7)：820-824.
[王磊,2005]王磊,黄文奇.求解工件车间调度问题的一种新的邻域搜索算法[J].计算机学报,2005,28(5)：809-816.
[王凌,2001]王凌,郑大钟.基于遗传算法的Job Shop调度研究进展[J].控制与决策,2001,16：641-646.
[王凌,2003]王凌.车间调度及其遗传算法[M].北京：清华大学出版社,2003.
[王凌,2008]王凌,刘波.微粒群优化与调度算法[M].北京：清华大学出版社,2008.
[王时龙,2012]王时龙,宋文艳,康玲,等.云制造环境下制造资源优化配置研究[J].计算机集成制造系统,2012,18(7)：1396-1405.
[王双记,2012]王双记.类电磁机制算法的改进与应用[D].西安：西安电子科技大学,2012.
[王万良,2001]王万良,吴启迪.基于Hopfield神经网络求解作业车间调度问题的新方 法[J].计算机集成制造系统,2001,7(12)：7-12.
[王万良,2004]王万良,吴启迪,宋毅.求解作业车间调度问题的改进自适应遗传算法[J].系统工程理论与实践,2004,(2)：58-62.
[王万良,2007]王万良,熊婧,王海燕,等.基于改进自适应蚁群算法的作业车间调度问题求解方法[C].中国人工智能学会第12届全国学术年会,哈尔滨,2007.
[王万良,2011]王万良,徐新黎,施莉娜,等.改进差分进化算法求解不确定流程车间调度问题[J].计算机集成制造系统,2011,17(3)：630-637.
[王小芹,2009]王小芹,王万良,徐新黎.一种求解Flow-Shop调度问题的混合量子进化算法[J].机电工程,2009,26(9)：5-8.
[王晓娟,2006]王晓娟.类电磁机制算法及其若干应用研究[D].武汉：华中科技大学,2006.
[王亚敏,2010]王亚敏,冀俊忠,潘全科.基于离散蛙跳算法的零空闲流水线调度问题求解[J].北京工业大学学报,2010,36(1)：124-130.
[王正成,2012]王正成,黄洋.面向服务链构建的云制造资源集成共享技术研究[J].中国机械工程,2012,23(11)：1324-1331.
[邬文尧,2009]邬文尧,蔡鸿明,姜丽红.柔性作业车间调度中的组合遗传优化研究[J].计算机工程与应用.2009,45(22)：183-186.
[吴澄,2008]吴澄,刘民.制造过程智能优化调度算法及其应用[M].北京：国防工业出版社,2008.
[吴大为,2005]吴大为,陆涛栋,刘晓冰,等.求解作业车间调度问题的并行模拟退火算法[J].计算机集成制造系统,2005,11(6)：847-850.
[吴昊,2012]吴昊,倪志伟,王会颖.基于MapReduce的蚁群算法[J].计算机集成制造系统,2012,18(7)：1503-1509.
[吴启迪,2011]吴启迪,康琦,汪镭,等.自然计算导论[M].上海：上海科学技术出版社,2011.
[吴秀丽,2006]吴秀丽.多目标柔性作业车间调度技术研究[D].西安：西北工业大学,2006.
[夏蔚军,2005]夏蔚军,吴智铭.基于混合微粒群优化的多目标柔性Job-shop调度[J].控制与决策,2005,20(2)：137-141.
[向峰,2012]向峰,胡业发,陶飞,等.云制造资源服务能耗评估及应用[J].计算机集成 制造系统,2012,18(9)：2109-2116.
[谢丽芳,2006]谢丽芳,费跃农.一种基于模拟退火算法的作业车间调度算法[J].中国制造业信息化,2006,35(9)：50-53.
[谢志强,2011]谢志强,邵侠,杨静.存在设备无关延迟约束的综合柔性调度算法[J].机械工程学报,2011,47(4)：177-185.
[徐新黎,2004]徐新黎,王万良,吴启迪.改进计算能量函数下作业车间调度的混沌神经网络方法[J].控制理论与应用,2004,21(2)：311-314.
[徐新黎,2010]徐新黎,应时彦,王万良.求解模糊柔性Job-shop调度问题的多智能体免疫算法[J].控制与决策,2010,25(2)：171-178,184.
[杨海成,2010]杨海成.云制造是一种制造服务[J].中国制造业信息化,2010,(6)：22-23.
[杨宏安,2011]杨宏安,孙启峰,李锦远.求解作业车间提前/拖期调度问题的一种新解码方法[J].计算机集成制造系统,2011,17(12)：2652-2659.
[杨男,2013]杨男,李东波,童一飞.云制造环境下基于动态描述逻辑的制造服务匹配研究[J].中国机械工程,2013,24(16)：2202-2207.
[姚嫣菲,2011]姚嫣菲.基于改进遗传算法的车间作业调度问题研究[D].杭州：浙江大学,2011.
[叶建芳,2008]叶建芳,王正肖,潘晓弘.免疫粒子群优化算法在车间作业调度中的应用[J].浙江大学学报(工学版),2008,42(5)：863-868,879.
[易军,2012]易军,李太福.求解作业车间调度的变邻域细菌觅食优化算法[J].机械工程学报,2012,48(12)：178-183.
[尹超,2012]尹超,张云,钟婷.面向新产品开发的云制造服务资源组合优选模型[J].计算机集成制造系统,2012,18(7)：1368-1378.
[尹胜,2011]尹胜,尹超,刘飞,等.云制造环境下外协加工资源集成服务模式及语义描述[J].计算机集成制造系统,2011,17(3)：525-532.
[越民义,1975]越民义,韩继业.n个零件在m台机床上的加工顺序问题(Ⅰ)[J].中国科学,1975,(5)：462-470.
[曾强,2010]曾强.离散制造企业批量生产车间调度智能优化研究[D].重庆：重庆大学,2010.
[张长胜,2009]张长胜,孙吉贵,欧阳丹彤,等.求解车间调度问题的自适应混合粒子群算法[J].计算机学报,2009,32(11)：2137-2146.
[张长水,1995]张长水,沈刚,阎平凡.解Job-shop调度问题的一个遗传算法[J].电子学报,1995,23(7)：1-5.
[张超勇,2004]张超勇,饶运清,刘向军,等.基于POX交叉的遗传算法求解Job-Shop调度问题[J].中国机械工程,2004,15(23)：2149-2153.
[张超勇,2007]张超勇.基于自然启发式算法的作业车间调度问题理论与应用研究[D].武汉：华中科技大学,2007.
[张超勇,2008]张超勇,管在林,刘琼,等.一种新调度类型及其在作业车间调度中的应用[J].机械工程学报,2008,44(10)：24-31.
[张超勇,2010]张超勇,董星,王晓娟,等.基于改进非支配排序遗传算法的多目标柔性作业车间调度[J].机械工程学报,2010,46(11)：156-164.
[张国辉,2009a]张国辉,高亮,李培根,等.改进遗传算法求解柔性作业车间调度问题[J].机械工程学报,2009,45(7)：145-151.
[张国辉,2009b]张国辉.柔性作业车间调度方法研究[D].武汉：华中科技大学,2009.
[张敬敏,2011]张敬敏,张有华,李霞.多目标柔性作业车间调度问题的混合差分算法[J].计算机工程与应用,2011,47(20)：245-248.
[张瑞,2008]张瑞,吴澄.求解大规模车间调度问题的一种分解优化算法[J].计算机集成制造系统,2008,14(8)：1559-1565.
[赵良辉,2009]赵良辉.作业车间调度问题的文化算法[J].计算机工程,2009,35(13)：196-198.
[赵楠,2012]赵楠,牛占文,郭伟.云制造环境中面向设计知识资源序列化组合的量子和声搜索算法[J].计算机集成制造系统,2012,18(7)：1435-1443.
[郑章元,2011]郑章元.蛙跳算法及其在置换流水车间调度中的应用研究[D].武汉：华中科技大学,2011.
[Amiri,2010] Amiri M, Zandieh M, Yazdani M, et al. A variable neighbourhood search algorithm for the flexible job-shop scheduling problem [J]. International Journal of Production Research,2010,48(19):5671-5689.
[Ashour,1967] Ashour S. A decomposition approach for the machine scheduling problem[J]. International Journal of Production Research,1967,6(2):109-122.
[Ashour,1973] Ashour S, Hiremath S R. A branch-and-bound approach to the job-shop scheduling problem[J]. International Journal of Production Research,1973,11(1): 47-58.
[Bagheri,2010] Bagheri A, Zandieh M, Mahdavi I, et al. An artificial immune algorithm for the flexible job-shop scheduling problem [J]. Future Generation Computer Systems, 2010,26(4):533-541.
[Balas,1969] Balas E. Machine sequencing via disjunctive graphs:an implicit enumeration algorithm [J]. Operations Research,1969,17(6):941-957.
[Balas,1998] Balas E, Vazacopoulos A. Guided local search with shifting bottleneck for job shop scheduling[J]. Management Science,1998,44(2):262-275.
[Baykasoglu,2002] Baykasoglu A. Linguistic-based meta-heuristic optimization model for flexible job shop scheduling[J]. International Journal of Production Research,2002, 40(17):4523-4543.
[Brandimarte,1993] Brandimarte P. Routing and scheduling in a flexible job shop by tabu search[J]. Annals of Operations Research,1993,41:157-183.
[Brucker,1990] Brucker P, Schlie R. Job-shop scheduling with multi-purpose machines[J]. Computing,1990,45(4):369-375.
[Brucker,1994] Brucker P, Jurisch B, Sievers B. A branch and bound algorithm for the job-shop scheduling problem[J]. Discrete Applied Mathematics,1994,49:107-127.
[Cao,2012]Cao Y, Song X Y, Zhang Y, et al. Application of ACOTS hybrid algorithm for job shop scheduling problems[C]. Proceedings of 2012 International Conference on Modeling, Identification and Control, Wuhan, China,2012.
[Carlier,1982] Carlier J. The one-machine sequencing problem[J]. European Journal of Operational Research,1982,11(1):42-47.
[Chen,1999] Chen H X, Ihlow J, Lehmann C. A genetic algorithm for flexible job-shop scheduling[C]. Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit, Michigan,1999.
[Cheng,1996] Cheng R W, Gen M, Tsujimura Y. A tutorial survey of job-shop scheduling problems using genetic algorithms-I. representation[J]. Computers & Industrial Engineering,1996,30(4):983-997.
[Cheng,1999] Cheng R W, Gen M, Tsujimura Y. A tutorial survey of job-shop scheduling problems using genetic algorithms, part II:hybrid genetic search strategies[J]. Computers & Industrial Engineering,1999,36:343-364.
[Chiang,2013] Chiang T C, Lin H J. A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling[J]. International Journal of Production Economics,2013,141(1):87-98.
[Chu,1992]Chu C, Portmann M C, Proth J M. A splitting-up approach to simplify job-shop scheduling problems[J]. International Journal of Production Research,1992,30(4): 859-870.
[Collinot,1989] Collinot A, Le Pape C. Controlling the behavior of knowledge sources within sonia[C]. Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences,1989,3:221-228.
[Collinot,1991] Collinot A, Le Pape C. Adapting the behavior of a job-shop scheduling system [J]. Decision Support Systems,1991,7(4):341-353.
[Dauzere-peres,1993] 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.
[Davis,1985] Davis L. Job-shop scheduling with genetic algorithms[C]. Proceedings of the 1st International Conference on Genetic Algorithms,1985.
[Demirkol,1997] Demirkol E, Mehta S, Uzsoy R. A computational study of shifting bottleneck procedures for shop scheduling problems[J]. Journal of Heuristics,1997,3:111-137.
[Dorigo,1996] Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics,1996, 26(1):29-41.
[Dorigo,1997] Dorigo M, Gambardella L M. Ant colony system:a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[Fernandes,2007] Fernandes S, Lourenco H R. A GRASP and branch-and-bound metaheuristic for the job-shop scheduling[J]. Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science,2007,4446:60-71.
[Fisher,1973] Fisher M L. Optimal solution of scheduling problems using lagrange multipliers:Part I[J]. Operations Research,1973,21:1114-1127.
[Fox,1983]Fox M S, Allen B P, Smith S F, et al. ISIS:a constraint-directed reasoning approach to job shop scheduling[C]. Proceedings of IEEE Conference on Trends and Applications, Gaithersburg, Maryland,1983.
[Fox,1984]Fox M S, Smith S F. ISIS-a knowledge-based system for factory scheduling[J]. Expert Systems,1984,1(1):25-49.
[Gao,2007]Gao J, Gen M, Sun L Y, et al. A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems[J]. Computers & Industrial Engineering,2007,53(1):149-162.
[Gao,2008]Gao J, Sun L Y, Gen M. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems[J]. Computers & Operations Research,2008,35(9):2892-2907.
[Gao,2011]Gao L, Zhang G H, Zhang L P, et al. An efficient memetic algorithm for solving the job shop scheduling problem[J]. Computers & Industrial Engineering,2011,60(4): 699-705.
[Ge,2008]Ge H W, Sun L, Liang Y C, et al. An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans,2008,38(2):358-368.
[Gholami,2009] Gholami M, Zandieh M. Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop[J]. Journal of Intelligent Manufacturing,2009, 20(4):481-498.
[Giffler,1960] Giffler B, Thompson G L. Algorithms for solving production-scheduling problems[J]. Operations Research,1960,8(4):487-503.
[Glover,1986] Glover F. Future paths for integer programming and links to artificial intelligence[J]. Computers & Operations Research,1986,13(5):533-549.
[Glover,1989] Glover F. Tabu search-part I[J]. ORSA Journal on Computing,1989,1(3): 190-206.
[Glover,1990] Glover F. Tabu search-part II[J]. ORSA Journal on Computing,1990,2(1): 4-32.
[Glover,1997] Glover F, Laguna M. Tabu search[M]. Dordrecht:Kluwer Academic Publishers,1997.
[Goncalves,2005] Goncalves J F, Magalhaes Mendes J J, Resende M G C. A hybrid genetic algorithm for the job shop scheduling problem[J]. European Journal of Operational Research,2005,167(1):77-95.
[Grabowski,2005] Grabowski J, Wodecki M. A very fast tabu search algorithm for job shop problem[C]. Rego C, Alidaee B. Metaheuristic Optimization via Memory and Evolution:Tabu Search and Scatter Search. Boston:Kluwer Academic Publishers, 2005:117-144.
[Graves,1981] Graves S C. A review of production scheduling[J]. Operations Research,1981, 29(4):646-675.
[Gu,2012]Gu W B, Tang D B, Zheng K. Minimizing makespan in job-shop scheduling problem using an improved adaptive particle swarm optimization algorithm[C]. In: 2012 24th Chinese Control and Decision Conference(CCDC),2012:3189-3193.
[Guimaraes,2006] Guimaraes K F, Fernandes M A. An approach for flexible job-shop scheduling with separable sequence-dependent setup time[C]. IEEE International Conference on Systems, Man and Cybernetics. Taipei,8-11 Oct 2006,5:3727-3731.
[Guo,2010]Guo H, Zhang L, Tao F, et al. Research on the measurement method of flexibility of resource service composition in cloud manufacturing[J]. Advanced Materials Research,2010,139-141:1451-1454.
[Gutowski,2005] Gutowski T, Murphy C, Allen D, et al. Environmentally benign manufacturing observations from Japan, Europe and the United States[J]. Journal of Cleaner Production,2005,13:1-17.
[Hansen,1986] Hansen P. The steepest ascent mildest descent heuristic for combinatorial programming[C]. Congress on Numerical Methods in Combinatorial Optimization, Capri, Italy,1986.
[Haupt,1989] Haupt R. A survey of priority rule-based scheduling[J]. OR Spektrum,1989, 11:3-16.
[Heinonen,2007] Heinonen J, Pettersson F. Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem[J]. Applied Mathematics and Computation,2007,187:989-998.
[Hmida,2010] Hmida A B, Haouari M, Huguet M, et al. Discrepancy search for the flexible job shop scheduling problem[J]. Computers & Operations Research,2010,37(12): 2192-2201.
[Ho,2005]Ho N, Tay J. Evolving dispatching rules for solving the flexible job-shop problem[C]. The 2005 IEEE Congress on Evolutionary Computation.2-5 Sept 2005,3: 2848-2855.
[Holland,1975] Holland J H. Adaptation in natural and artificial systems[M]. Ann Arbor:The University of Michigan Press,1975.
[Holthaus,1997] Holthaus O, Rajendran C. Efficient dispatching rules for scheduling in a job shop[J]. International Journal of Production Economics,1997,48:87-105.
[Hou,1994]Hou E S H, Ansari N, Ren H. A genetic algorithm for multiprocessor scheduling[J]. IEEE Transactions on Parallel and Distributed Systems,1994,5(2): 113-120.
[Huang,2004] Huang W Q, Yin A H. An improved shifting bottleneck procedure for the job shop scheduling problem[J]. Computers & Operations Research,2004,31:2093-2110.
[Huang,2008] Huang K L, Liao C J. Ant colony optimization combined with taboo search for the job shop scheduling problem[J]. Computers & Operations Research,2008,35: 1030-1046.
[Hurink,1994] Hurink J, Jurisch B, Thole M. Tabu search for the job-shop scheduling problem with multi-purpose machines[J]. Operations-Research-Spektrum,1994,15(4):205-215.
[Imanipour,2006] Imanipour N. Modeling & solving flexible job shop problem with sequence dependent setup times[C]. International Conference on Service Systems and Service Management. Troyes,25-27 Oct 2006,2:1205-1210.
[Jamili,2011] Jamili A, Shafia M A, Tavakkoli-moghaddam R. A hybridization of simulated annealing and electromagnetism-like mechanism for a periodic job shop scheduling problem[J]. Expert Systems with Applications,2011,38(5):5895-5901.
[Jansen,2000] Jansen K, Mastrolilli M, Solis-oba R. Approximation algorithms for flexible job shop problems[J]. LATIN 2000:Theoretical Informatics Lecture Notes in Computer Science.2000,1776:68-77.
[Johnson,1954] Johnson S M. Optimal two-and three-stage production schedules with setup times included[J]. Naval Research Logistics Quarterly,1954,1(1):61-68.
[Kacem,2001] Kacem I, Hammadi S, Borne P. Approach by localization and genetic manipulation algorthm for flexible job-shop scheduling problem[C]. IEEE International Conference on Systems, Man, and Cybernetics. Tucson, AZ,07-10 Oct 2001,4: 2599-2604.
[Kacem,2002] Kacem I, Hammadi S, Borne P. Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems[J]. IEEE Transactions on Systems, Man and Cybernetics, Part C:Applications and Reviews, 2002,32(1):1-13.
[Karimi,2012] Karimi H, Rahmati S H A, Zandieh M. An efficient knowledge-based algorithm for the flexible job shop scheduling problem[J]. Knowledge-Based Systems, 2012,36:236-244.
[Kennedy,1995] Kennedy J, Eberhart R. Particle swarm optimization[C]. IEEE International Conference on Neural Networks,1995,4:1942-1948.
[Kirkpatrick,1983] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science, New Series,1983,220(4598):671-680.
[Kruger,1995] Kruger K, Shakhlevich N V, Sotskov Y N, et al. A heuristic decomposition algorithm for scheduling problems on mixed graphs[J]. Journal of the Operational Research Society,1995,46(12):1481-1497.
[Lei,2008]Lei D M. A Pareto archive particle swarm optimization for multi-objective job shop scheduling[J]. Computers & Industrial Engineering,2008,54:960-971.
[Li,2011a]Li J Q, Pan QK, Suganthan P N, et al. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem[J]. International Journal of Advanced Manufacturing Technology,2011,52(5-8):683-697.
[Li,2011b]Li J, Pan Q, Xie S, et al. A hybrid artificial bee colony algorithm for flexible job shop scheduling problems[J]. Int. J. of Computers Communications & Control,2011, (2):286-296.
[Li,2012a]Li J Q, Pan Q K. Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity[J]. Applied Soft Computing,2012,12(9): 2896-2912.
[Li,2012b]Li J Q, Xie S X, Sun T, et al. Solving fuzzy job-shop scheduling problem by genetic algorithm[C]. Control and Decision Conference, Chinese,2012.
[Lin,2010]Lin T L, Horng S J, Kao T W, et al. An efficient job-shop scheduling algorithm based on particle swarm optimization[J]. Expert Systems with Applications,2010,37: 2629-2636.
[Lobato,2012] Lobato F S, Gedraite R, Neiro S M S. Solution of flow shop scheduling problems using the differential evolution algorithm[C]. IN:EngOpt 2012-3rd, International Conference on Engineering Optimization, Rio de Janeiro, Brazil,2012.
[Luh,2000]Luh P B, Zhao X, Wang Y J, et al. Lagrangian relaxation neural networks for job shop scheduling[J]. IEEE Transactions on Robotics and Automation,2000,16(1): 78-88.
[Manne,1960] Manne A S. On the job-shop scheduling problem[J]. Operations Research, 1960,8:219-223.
[Mastrolilli,2000] Mastrolilli M, Gambardella L M. Effective neighborhood functions for the flexible job shop problem[J]. Journal of scheduling,2000,3(1):3-20.
[Mati,2001] Mati Y, Rezg N, Xie X L. An integrated greedy heuristic for a flexible job shop scheduling problem[C]. IEEE International Conference on Systems, Man, and Cybernetics. Tucson, AZ,07-10 Oct 2001,4:2534-2539.
[Matsuo,1988] Matsuo H, Suh C J, Sullivan R S. A controlled search simulated annealing method for the general job-shop scheduling problem[R]. Working Paper,03-04-88, Graduate School of Business, University of Texas at Austin, Austin, Texas, USA, 1988.
[Melab,2012] Melab N, Chakroun I, Mezmaz M, et al. A GPU-accelerated branch-and-bound algorithm for the flow-shop scheduling problem[C].2012 IEEE International Conference on Cluster Computing, France,2012.
[Metropolis,1953] Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equation of state calculations by fast computing machines[J]. Journal of Chemical Physics,1953,21(6): 1087-1092.
[Mohammad,2007] Mohammad S M, Parviz F. Flexible job shop scheduling by tabu search algorithms[J]. International Journal of Advanced Manufacturing Technology,2007,32: 563-570.
[Mohammad,2011] Mohammad R, Raeesi N, Ziad K. A machine operation lists based memetic algorithm for job shop scheduling[C]. IEEE Congress on Evolutionary Computation(CEC), New Orleans, LA, USA,2011:2436-2443.
[Mohammad,2012] Mohammad R, Raeesi N, Ziad K. A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic[J]. Memetic Computing, 2012,4(3):231-245.
[Moslehi,2011] Moslehi G, Mahnam M. A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search[J]. International Journal of Production Economics,2011,129:14-22.
[Naderi,2010] Naderi B, Tavakkoli-moghaddam R, Khalili M. Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan[J]. Knowledge-Based Systems, 2010,23:77-85.
[Najid,2002] Najid N M, Dauzere-peres S, Zaidat A. A modified simulated annealing method for flexible job shop scheduling problem [C].2002 IEEE International Conference on Systems, Man and Cybernetics,2002.
[Neifar,2006] Neifar F, Gzara M, Moukrim A, et al. Hybrid evolutionary algorithm with insertion heuristics for the flexible job shop problem[C]. International Conference on Service Systems and Service Management. Troyes,25-27 Oct 2006,2:1211-1216.
[Nowicki,1996] Nowicki E, Smutnicki C. A fast taboo search algorithm for the job shop problem[J]. Management Science,1996,42(6):797-813.
[Nowicki,2002] Nowicki E, Smutnicki C. Some new tools to solve the job-shop problem [R]. Technical Report 60/02, Institute of Engineering Cybernetics, Wroclaw University of Technology,2002.
[Ong,2005]Ong Z X, Tay J C, Kwoh C K. Applying the clonal selection principle to find flexible job-shop schedules[J]. Artificial Immune Systems:Lecture Notes in Computer Science.2005,3627:442-455.
[Pacino,2011] Pacino D, Hentenryck P V. Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling[C]. Proceedings of the Twenty-Second international joint conference on Artificial Intelligence.2011,3: 1997-2002.
[Panwalkar,1977] Panwalkar S S, Iskander W. A survey of scheduling rules[J]. Operations Research,1977,25(1):45-61.
[Park,2003] Park B J, Choi H R, Kim H S. A hybrid genetic algorithm for the job shop scheduling problems[J]. Computers & Industrial Engineering,2003,45(4):597-613.
[Paulli,1995] Paulli J. A hierarchical approach for the FMS scheduling problem[J]. European Journal of Operational Research,1995,86(1-5):32-42.
[Pesch,1996] Pesch E, Tetzlaff U A W. Constraint propagation based scheduling of job shops[J]. Journal on Computing,1996,8(2):144-157.
[Pezzella,2000] Pezzella F, Merelli E. A tabu search method guided by shifting bottleneck for the job shop scheduling problem[J]. European Journal of Operational Research,2000, 120:297-310.
[Pezzella,2008] Pezzella F, Morganti G, Ciaschetti G. A genetic algorithm for the flexible job-shop scheduling problem[J]. Computers & Operations Research,2008,35: 3202-3212.
[Pinedo,2007] Pinedo M调度：原理、算法和系统[M].北京：清华大学出版社,2007.
[Ponsich,2013] Ponsich A, Coello Coello C A. A hybrid differential evolution-tabu search algorithm for the solution of job-shop scheduling problems[J]. Applied Soft Computing,2013,13(1):462-474.
[Priore,2003] Priore P, Fuente D, Pino R, et al. Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning[J]. Integrated Manufacturing Systems,2003,14(2):160-168.
[Qing-dao-er-ji,2012] Qing-dao-er-ji R, Wang Y P. A new hybrid genetic algorithm for job shop scheduling problem[J]. Computers & Operations Research,2012,39:2291-2299.
[Rahmati,2012] Rahmati S H A, Zandieh M. A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem[J]. International Journal of Advanced Manufacturing Technology,2012,58(9-12):1115-1129.
[Rego,2009] Rego C, Duarte R. A filter-and-fan approach to the job shop scheduling problem[J]. European Journal of Operational Research,2009,194(3):650-662.
[Rossi,2007] Rossi A, Dini G. Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method[J]. Robotics and Computer-Integrated Manufacturing,2007,23(5):503-516.
[Saidi-mehrabad,2007] Saidi-mehrabad M, Fattahi P. Flexible job shop scheduling with tabu search algorithms[J]. International Journal of Advanced Manufacturing Technology, 2007,32(5-6):563-570.
[Santosa,2012] Santosa B, Nurkhalida L. A cross entropy-genetic algorithm approach for multi objective job shop scheduling problem[C]. Proceedings of the 2012 International Conference on Industrial ngineering and Operations Management, Istanbul, Turkey, 2012.
[Scrich,2004] Scrich C R, Armentano V A, Laguna M. Tardiness minimization in a flexible job shop:a tabu search approach[J]. Journal of Intelligent Manufacturing,2004,15(1): 103-115.
[Sha,2006]Sha D Y, Hsu C Y. A hybrid particle swarm optimization for job shop scheduling problem[J]. Computers & Industrial Engineering,2006,51:791-808.
[Stora,1995] Storn R, Price K. Differential evolution:a simple and efficient adaptive scheme for global optimization over continuous spaces[R]. Berkeley, International Computer Science Institute,1995.
[Subramaniam,2000] Subramaniam V, Ramesh T, Lee G K, et al. Job shop scheduling with dynamic fuzzy selection of dispatching rules[J]. International Journal of Advanced Manufacturing Technology,2000,16:759-764.
[Taillard,1989] Taillard E D. Parallel taboo search technique for the jobshop scheduling problem[R]. Technical Report ORWP 89/11, Departement de mathematiques, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland,1989.
[Tamaki,2001] Tamaki H, Ono T, Murao H, et al. Modeling and genetic solution of a class of flexible job shop scheduling problems[C]. International Conference on Emerging Technologies and Factory Automation. Antibes-Juan les Pins, France,15-18 Oct 2001, 2:343-350.
[Tang,2011] Tang J C, Zhang G J, Lin B B, et al. A hybrid algorithm for flexible job-shop scheduling problem[J]. Procedia Engineering,2011,15:3678-3683.
[Teekeng,2011] Teekeng W, Thammano A. A combination of shuffled frog leaping and fuzzy logic for flexible job-shop scheduling problems[J]. Procedia Computer Science,2011, 6:69-75.
[Thamilselvan,2012] Thamilselvan R, Balasubramanie P. Integration of genetic algorithm with tabu search for job shop scheduling with unordered subsequence exchange crossover[J]. Journal of Computer Science,2012,8(5):681-693.
[Van,1988]Van Laarhoven P J M, Aarts E H L, Lenstra J K. Job-shop scheduling by simulated annealing[R]. Report OSR8809, Centrum voor Wiskunde en Informatica, Amsterdam, Netherlands,1988.
[Van,1991]Van Hulle M M. A goal programming network for mixed integer linear programming:a case study for the job-shop scheduling problem [J]. International Journal of Neural Systems,1991,2(3):201-209.
[Van,1992]Van Laarhoven P J M, Aarts E H L, Lenstra J K. Job shop scheduling by simulated annealing[J]. Operations Research,1992,40(1):113-125.
[Vilcot,2006] Vilcot G, Billaut J, Esswein C. A genetic algorithm for a bicriteria flexible job shop scheduling problem[C]. International Conference on Service Systems and Service Management. Troyes, Oct 2006,2:1240-1244.
[Wang,2008] Wang S J, Zhou B H, Xi L F. A filtered-beam-search-based heuristic algorithm for flexible job-shop scheduling problem[J]. International Journal of Production Research,2008,46(11):3027-3058.
[Wang,2011] Wang L, Tang D B. An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem[J]. Expert Systems with Applications,2011,38(6):7243-7250.
[Wang,2012a] Wang L, Wang S Y, Xu Y, et al. A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem[J]. Computers & Industrial Engineering,2012,62(4):917-926.
[Wang,2012b] Wang L, Zhou G, Xu Y, et al. An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling[J]. International Journal of Advanced Manufacturing Technology,2012,60(9-12):1111-1123.
[Watanabe,2005] Watanabe M, Ida K, Gen M. A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem[J]. Computers & Industrial Engineering,2005,48(4):743-752.
[Wu,2005]Wu Z B, Weng M X. Multiagent scheduling method with earliness and tardiness objectives in flexible job shops[J]. IEEE Transactions on Systems, Man, and Cybernetics-part b:cybernetics,2005,35(2):293-301.
[Xia,2005]Xia W J, Wu Z M. An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems[J]. Computers & Industrial Engineering,2005, 48:409-425.
[Xing,2010] Xing L N, Chen Y W, Wang P, et al. A knowledge-based ant colony optimization for flexible job shop scheduling problems[J]. Applied Soft Computing,2010,10: 888-896.
[Xu,2012]Xu Y, Wang L, Wang S Y, et al. An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines[J]. Engineering Optimization,2013,45(12):1409-1430.
[Zhang,2005] Zhang H P, Gen M. Multistage-based genetic algorithm for flexible job-shop scheduling problem[J]. Journal of Complexity Interna-tional,2005,11:223-232.
[Zhang,2007] Zhang C Y, Li P G, Guan Z L, et al. A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem[J]. Computers & Operations Research,2007,34:3229-3242.
[Zhang,2008a] Zhang C Y, Rao Y Q, Li P G. An effective hybrid genetic algorithm for the job shop scheduling problem [J]. International Journal of Advanced Manufacturing Technology,2008,39:965-974.
[Zhang,2008b] Zhang G H, Shi Y, Gao L. A genetic algorithm and tabu search for solving flexible job shop schedules[C]. International Symposium on Computational Intelligence and Design,2008.
[Zhang,2009] Zhang G H, Shao X Y, Li P G, et al. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J]. Computers & Industrial Engineering,2009,56(4):1309-1318.
[Zhang,2010] Zhang L, Guo H, Tao F, et al. Flexible management of resource service composition in cloud manufacturing[C].2010 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao,7-10 Dec 2010: 2278-2282.
[Zhang,2011] Zhang G H, Gao L, Shi Y. An effective genetic algorithm for the flexible job-shop scheduling problem[J]. Expert Systems with Applications,2011,38(4): 3563-3573.
[Zhang,2012] Zhang Q, Manier H, Manier M A. A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times[J]. Computers & Operations Research,2012,39(7):1713-1723.
[Zhao,2001] Zhao C W, Wu ZM.A genetic algorithm approach to the scheduling of fmss with multiple routes[J]. International Journal of Flexible Manufacturing Systems, 2001,13(1):71-88.
[Zhou,2001] Zhou H, Feng Y C, Han L M. The hybrid heuristic genetic algorithm for job shop scheduling[J]. Computers & Industrial Engineering,2001,40:191-200.
[Zhu,2010]Zhu K C, Jiang M Y. The optimization of job shop scheduling problem based on artificial fish swarm algorithm with tabu search strategy[C]. In:Third International Workshop on Advanced Computational Intelligence,2010,323-327.
[Zribi,2004] Zribi N, Kacem I, El Kamel A, et al. Optimization by phases for the flexible job-shop scheduling problem[C].5th Control Conference. Melbourne, Victoria, Australia,20-23 July 2004, (3):1889-1895.
[Zribi,2005] Zribi N, Borne P. Hybrid genetic algorithm for the flexible job-shop problem under maintenance constraints[J]. Advances in Natural Computation, Lecture Notes in Computer Science.2005,3612:259-268.
[Zribi,2006] Zribi N, El Kamel A, Borne P. Total tardiness in a flexible job-shop[C]. IMACS Multiconference on Computational Engineering in Systems Applications. Beijing, China,4-6 Oct 2006:1543-1549.
[Zribi,2007] Zribi N, Kacem I, El Kamel A, et al. Assignment and scheduling in flexible job-shops by hierarchical optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews,2007,7(4):652-661.
[Zuo,2012]Zuo X Q, Wang C L, Tan W. Two heads are better than one:an AIS- and TS-based hybrid strategy for job shop scheduling problems[J]. International Journal of Advanced Manufacturing Technology,2012,63(1-4):155-158.
[白俊杰,2010]白俊杰,王宁生,唐敦兵.一种求解多目标柔性作业车间调度的改进粒子群算法[J].南京航空航天大学学报,2010,42(4)：447-453.
[蔡良伟,2010]蔡良伟,李霞.基于混合蛙跳算法的作业车间调度优化[J].深圳大学学报(理工版),2010,27(4)：391-395.
[陈彩丽,2010]陈彩丽.基于遗传算法和仿真的车间调度优化系统研究与开发[D].杭州：浙江大学,2010.
[陈成,2011]陈成,邢立宁.求解柔性作业车间调度问题的遗传-蚁群算法[J].计算机集成制造系统,2011,17(3)：615-621.
[陈恩红,1998]陈恩红,薛瀚宏.基于约束满足的Job-Shop调度问题求解方法研究[J].软件学报,1998,9(12)：946-948.
[陈钢,2007]陈钢,高杰,孙林岩.带瓶颈移动法的混合遗传算法求解柔性作业车间调度[J].系统工程,2007,25(9)：91-97.
[陈荣秋,1995]陈荣秋.生产计划与控制：概念、方法与系统[M].武汉：华中理工大学出版社,1995.
[程方晓,2011]程方晓.基于自适应保持多样性遗传算法的汽车动力传动系多目标优化[D].吉林：吉林大学,2011.
[崔静静,2011]崔静静,孙延明,车兰秀.改进细菌觅食算法求解车间作业调度问题[J].计算机应用研究,2011,28(9)：3324-3326.
[方开泰,1994]方开泰.均匀设计与均匀设计表[M].北京：科学出版社,1994.
[方水良,2012]方水良,姚嫣菲,赵诗奎.柔性车间调度的改进遗传算法[J].浙江大学学报(工学版),2012,46(4)：629-635.
[高亮,2006]高亮,高海兵,周驰.基于粒子群优化的开放式车间调度[J].机械工程学报,2006,42(2)：129-134.
[谷峰,2005]谷峰,陈华平,卢冰原.病毒遗传算法在柔性工作车间调度中的应用[J].系统工程与电子技术,2005,27(11)：1953-1956.
[谷峰,2006]谷峰,陈华平,卢冰原.基于均匀设计的多目标遗传算法在柔性工作车间 调度中的应用[J].系统工程理论方法与应用,2006,15(6)：548-551.
[顾新建,2010]顾新建,张栋,纪杨建,等.制造业服务化和信息化融合技术[J].计算机集成制造系统,2010,16(11)：2530-2536.
[郭冬芬,2007]郭冬芬,李铁克.基于约束满足的车间调度算法综述[J].计算机集成制造系统,2007,13(1)： 117-125.
[何大阔,2005]何大阔,王福利,贾明兴.遗传算法初始种群与操作参数的均匀设计[J].东北大学学报：自然科学版,2005,26(9)：828-831.
[何建佳,2009]何建佳,徐福缘,叶伟.置换流水车间调度的文化量子粒子群算法[J].机械设计与制造,2009,(8)：17-19.
[何霆,2000]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.
[黄明,2009]黄明,刘鹏飞,梁旭.面向作业车间的自适应非均匀窗口蚁群算法[J].计算机集成制造系统,2009,15(10)：1973-1978.
[黄明,2012]黄明,刘鹏飞.混合生产形态下作业车间调度问题研究[M].北京：北京师范大学出版社,2012.
[黄志,2005]黄志,黄文奇.一种基于禁忌搜索方法的作业车间调度[J].华中科技大学学报(自然科学版),2005,33(12)：109-111.
[黄竹君,2009]黄竹君.基于启发式调度规则的车间作业计划算法及仿真研究[D].武汉：武汉科技大学,2009.
[纪树新,1995]纪树新.基于遗传算法的车间作业调度系统研究[D].杭州：浙江大学,1995.
[纪树新,1997]纪树新,钱积新,孙优贤.车间作业调度遗传算法中的编码研究[J].信息与控制,1997,26(5)：393-400.
[贾兆红,2008]贾兆红.粒子群优化算法在柔性作业车间调度中的应用研究[D].合肥：中国科学技术大学,2008.
[姜思杰,2003]姜思杰,张付亮,王孔茂.基于遗传和禁忌算法求解一类车间调度问题[J].计算机集成制造系统,2003,9(11)：984-988.
[李伯虎,2010]李伯虎,张霖,王时龙,等.云制造—面向服务的网络化制造新模式[J].计算机集成制造系统,2010,(1)：1-7,16.
[李春泉,2011]李春泉,尚玉玲,胡春杨.云制造的体系结构及其关键技术研究[J].组合 机床与自动化加工技术,2011,(7)：104-107,112.
[李海波,2013]李海波.云制造环境下基于工作流的多粒度资源组合方法[J].计算机集成制造系统,2013,19(1)：210-216.
[李慧芳,2012]李慧芳,董训,宋长刚.制造云服务智能搜索与匹配方法[J].计算机集成制造系统,2012,18(7)：1485-1493.
[李飒,2000]李飒,王云莉,段广洪,等.基于多智能体结构的车间调度方法[J].机械科学与技术,2000,19(6)：956-958.
[李铁克,2010]李铁克,王伟玲,张文学.基于文化遗传算法求解柔性作业车间调度问题[J].计算机集成制造系统,2010,16(4)：861-866.
[李伟平,2011]李伟平,林慧苹,莫同,等.云制造中的关键技术分析[J].制造业自动化,2011,33(1)：7-10.
[梁昌勇,2009]梁昌勇,陆青,张恩桥,等.基于均匀设计的多智能体遗传算法研究[J].系统工程学报,2009,24(1)：109-113.
[梁旭,2005a]梁旭,黄明,常征.求解车间调度问题的一种新遗传退火混合策略[J].计算机集成制造系统,2005,11(6)：851-854.
[梁旭,2005b]梁旭,黄明.禁忌-并行遗传算法在作业车间调度中的应用[J].计算机集成制造系统,2005,11(5)：678-681.
[刘爱军,2011]刘爱军.离散制造车间多生产模式下作业调度研究[D].重庆：重庆大学,2011.
[刘爱军,2012]刘爱军,杨育,邢青松,等.含精英策略的小生境遗传退火算法研究及其应用[J].中国机械工程,2012,23(5)：556-563.
[刘海霞,2008]刘海霞,李仁旺,李学,等.基于遗传模拟退火算法的制造网格资源调度策略[J].计算机工程与应用,2008,44(6)：234-236,242.
[刘民,2000]刘民,吴澄,杨英杰.并行多机调度问题的一种基于组合规则的遗传算法[J].电子学报,2000,28(5)：1-3.
[刘敏,2011]刘敏,张超勇,张国军,等.基于混合粒子群优化算法的置换流水车间调度问题研究[J].中国机械工程,2011,22(17)：2048-2053.
[刘强,2013]刘强,王磊,陈新度,等.云制造服务平台的资源使用及访问控制[J].计算机集成制造系统,2013,19(6)：1414-1422.
[刘琼,2009]刘琼,张超勇,饶运清,等.改进遗传算法解决柔性作业车间调度问题[J].工 业工程与管理,2009,14(2)：59-66.
[刘士军,2012]刘士军,曲本科,武蕾,等.自组织云制造资源聚集框架与多维属性区间搜索方法研究[J].计算机辅助设计与图形学学报,2012,24(3)：299-307.
[刘卫宁,2013]刘卫宁,刘波,孙棣华.面向多任务的制造云服务组合[J].计算机集成制造系统,2013,19(1)：199-209.
[刘志峰,2004]刘志峰,梁峰,朱华炳.基于产品结构树的CAPP系统开发技术[J].农业机械学报,2004,35(2)：92-95.
[刘志勇,2010]刘志勇,吕文阁,谢庆华,等.应用改进蚁群算法求解柔性作业车间调度问题[J].工业工程与管理,2010,15(3)：115-119.
[马川,2009]马川,王涛,王宝文,等.一种基于混沌神经网络的作业车间调度算法[J].机床与液压,2009,37(7)：11-14,20.
[孟祥旭,2011]孟祥旭,刘士军,武蕾,等.云制造模式与支撑技术[J].山东大学学报(工学版),2011,41(5)：13-20.
[潘全科,2006]潘全科,王文宏,朱剑英.一类解决车间调度问题的遗传退火算法[J].机械科学与技术,2006,25(3)：317-321.
[潘全科,2007]潘全科,王文宏,朱剑英.解决无等待流水车间调度问题的离散粒子群优化算法[J].计算机集成制造系统,2007,13(6)：1127-1130,1136.
[潘全科,2010]潘全科,王凌,高亮,等.基于差分进化与块结构邻域的作业车间调度优化[J].机械工程学报,2010,46(22)：182-188.
[庞哈利,2003]庞哈利.柔性Job shop集成化计划调度模型及其求解算法[J].控制与决策,2003,18(1)：34-39.
[彭传勇,2006]彭传勇,高亮,邵新宇,等.求解作业车间调度问题的广义粒子群优化算法[J].计算机集成制造系统,2006,12(6)：911-917,923.
[彭海波,2006]彭海波,基于Petri网的机加工车间建模与仿真研究[D].武汉：武汉科技大学,2006.
[任磊,2011]任磊,张霖,张雅彬,等.云制造资源虚拟化研究[J].计算机集成制造系统,2011,17(3)：511-518.
[任彦君,2010]任彦君,黎冰,顾幸生.改进的人工鱼群算法在置换Flow Shop调度中的应用[J].华东理工大学学报(自然科学版),2010,36(1)：93-98.
[上官春霞,2008]上官春霞,周泓,师瑞峰,等.作业车间排序重调度问题及其改进修 复约束满足算法[J].计算机集成制造系统,2008,14(9)：1742-1751,1773.
[宋强磊,2012]宋强磊,车阿大.量子进化算法在生产调度中的应用综述[J].计算机应用研究,2012,29(5)：1601-1605.
[苏子林,2008]苏子林.车间调度问题及其进化算法分析[J].机械工程学报,2008,44(8)：242-247.
[孙志峻,2001]孙志峻,朱剑英.具有柔性加工路径的作业车间智能优化调度[J].机械科学与技术,2001,20(6)：931-932,935.
[邰丽君,2013]邰丽君,胡如夫,赵韩,等.面向云制造服务的制造资源多目标动态优化调度[J].中国机械工程,2013,24(12)：1616-1622.
[谈峰,2008]谈峰,王伟.多种群差分进化算法及在柔性车间作业调度中的应用[J].湖南农业大学学报(自然科学版),2008,34(1)：105-108.
[覃朝勇,2010]覃朝勇,刘向,郑建国.求解多目标job-shop生产调度问题的量子进化算法[J].计算机应用研究,2010,27(3)：849-852.
[唐启义,2010]唐启义.DPS数据处理系统：实验设计、统计分析及数据挖掘[M].北京：科学出版社,2010.
[陶飞,2011]陶飞,张霖,郭华,等.云制造特征及云服务组合关键问题研究[J].计算机集成制造系统,2011,17(3)：477-486.
[王常青,2004]王常青,操云甫,戴国忠.用双向收敛蚁群算法解作业车间调度问题[J].计算机集成制造系统,2004,10(7)：820-824.
[王磊,2005]王磊,黄文奇.求解工件车间调度问题的一种新的邻域搜索算法[J].计算机学报,2005,28(5)：809-816.
[王凌,2001]王凌,郑大钟.基于遗传算法的Job Shop调度研究进展[J].控制与决策,2001,16：641-646.
[王凌,2003]王凌.车间调度及其遗传算法[M].北京：清华大学出版社,2003.
[王凌,2008]王凌,刘波.微粒群优化与调度算法[M].北京：清华大学出版社,2008.
[王时龙,2012]王时龙,宋文艳,康玲,等.云制造环境下制造资源优化配置研究[J].计算机集成制造系统,2012,18(7)：1396-1405.
[王双记,2012]王双记.类电磁机制算法的改进与应用[D].西安：西安电子科技大学,2012.
[王万良,2001]王万良,吴启迪.基于Hopfield神经网络求解作业车间调度问题的新方 法[J].计算机集成制造系统,2001,7(12)：7-12.
[王万良,2004]王万良,吴启迪,宋毅.求解作业车间调度问题的改进自适应遗传算法[J].系统工程理论与实践,2004,(2)：58-62.
[王万良,2007]王万良,熊婧,王海燕,等.基于改进自适应蚁群算法的作业车间调度问题求解方法[C].中国人工智能学会第12届全国学术年会,哈尔滨,2007.
[王万良,2011]王万良,徐新黎,施莉娜,等.改进差分进化算法求解不确定流程车间调度问题[J].计算机集成制造系统,2011,17(3)：630-637.
[王小芹,2009]王小芹,王万良,徐新黎.一种求解Flow-Shop调度问题的混合量子进化算法[J].机电工程,2009,26(9)：5-8.
[王晓娟,2006]王晓娟.类电磁机制算法及其若干应用研究[D].武汉：华中科技大学,2006.
[王亚敏,2010]王亚敏,冀俊忠,潘全科.基于离散蛙跳算法的零空闲流水线调度问题求解[J].北京工业大学学报,2010,36(1)：124-130.
[王正成,2012]王正成,黄洋.面向服务链构建的云制造资源集成共享技术研究[J].中国机械工程,2012,23(11)：1324-1331.
[邬文尧,2009]邬文尧,蔡鸿明,姜丽红.柔性作业车间调度中的组合遗传优化研究[J].计算机工程与应用.2009,45(22)：183-186.
[吴澄,2008]吴澄,刘民.制造过程智能优化调度算法及其应用[M].北京：国防工业出版社,2008.
[吴大为,2005]吴大为,陆涛栋,刘晓冰,等.求解作业车间调度问题的并行模拟退火算法[J].计算机集成制造系统,2005,11(6)：847-850.
[吴昊,2012]吴昊,倪志伟,王会颖.基于MapReduce的蚁群算法[J].计算机集成制造系统,2012,18(7)：1503-1509.
[吴启迪,2011]吴启迪,康琦,汪镭,等.自然计算导论[M].上海：上海科学技术出版社,2011.
[吴秀丽,2006]吴秀丽.多目标柔性作业车间调度技术研究[D].西安：西北工业大学,2006.
[夏蔚军,2005]夏蔚军,吴智铭.基于混合微粒群优化的多目标柔性Job-shop调度[J].控制与决策,2005,20(2)：137-141.
[向峰,2012]向峰,胡业发,陶飞,等.云制造资源服务能耗评估及应用[J].计算机集成 制造系统,2012,18(9)：2109-2116.
[谢丽芳,2006]谢丽芳,费跃农.一种基于模拟退火算法的作业车间调度算法[J].中国制造业信息化,2006,35(9)：50-53.
[谢志强,2011]谢志强,邵侠,杨静.存在设备无关延迟约束的综合柔性调度算法[J].机械工程学报,2011,47(4)：177-185.
[徐新黎,2004]徐新黎,王万良,吴启迪.改进计算能量函数下作业车间调度的混沌神经网络方法[J].控制理论与应用,2004,21(2)：311-314.
[徐新黎,2010]徐新黎,应时彦,王万良.求解模糊柔性Job-shop调度问题的多智能体免疫算法[J].控制与决策,2010,25(2)：171-178,184.
[杨海成,2010]杨海成.云制造是一种制造服务[J].中国制造业信息化,2010,(6)：22-23.
[杨宏安,2011]杨宏安,孙启峰,李锦远.求解作业车间提前/拖期调度问题的一种新解码方法[J].计算机集成制造系统,2011,17(12)：2652-2659.
[杨男,2013]杨男,李东波,童一飞.云制造环境下基于动态描述逻辑的制造服务匹配研究[J].中国机械工程,2013,24(16)：2202-2207.
[姚嫣菲,2011]姚嫣菲.基于改进遗传算法的车间作业调度问题研究[D].杭州：浙江大学,2011.
[叶建芳,2008]叶建芳,王正肖,潘晓弘.免疫粒子群优化算法在车间作业调度中的应用[J].浙江大学学报(工学版),2008,42(5)：863-868,879.
[易军,2012]易军,李太福.求解作业车间调度的变邻域细菌觅食优化算法[J].机械工程学报,2012,48(12)：178-183.
[尹超,2012]尹超,张云,钟婷.面向新产品开发的云制造服务资源组合优选模型[J].计算机集成制造系统,2012,18(7)：1368-1378.
[尹胜,2011]尹胜,尹超,刘飞,等.云制造环境下外协加工资源集成服务模式及语义描述[J].计算机集成制造系统,2011,17(3)：525-532.
[越民义,1975]越民义,韩继业.n个零件在m台机床上的加工顺序问题(Ⅰ)[J].中国科学,1975,(5)：462-470.
[曾强,2010]曾强.离散制造企业批量生产车间调度智能优化研究[D].重庆：重庆大学,2010.
[张长胜,2009]张长胜,孙吉贵,欧阳丹彤,等.求解车间调度问题的自适应混合粒子群算法[J].计算机学报,2009,32(11)：2137-2146.
[张长水,1995]张长水,沈刚,阎平凡.解Job-shop调度问题的一个遗传算法[J].电子学报,1995,23(7)：1-5.
[张超勇,2004]张超勇,饶运清,刘向军,等.基于POX交叉的遗传算法求解Job-Shop调度问题[J].中国机械工程,2004,15(23)：2149-2153.
[张超勇,2007]张超勇.基于自然启发式算法的作业车间调度问题理论与应用研究[D].武汉：华中科技大学,2007.
[张超勇,2008]张超勇,管在林,刘琼,等.一种新调度类型及其在作业车间调度中的应用[J].机械工程学报,2008,44(10)：24-31.
[张超勇,2010]张超勇,董星,王晓娟,等.基于改进非支配排序遗传算法的多目标柔性作业车间调度[J].机械工程学报,2010,46(11)：156-164.
[张国辉,2009a]张国辉,高亮,李培根,等.改进遗传算法求解柔性作业车间调度问题[J].机械工程学报,2009,45(7)：145-151.
[张国辉,2009b]张国辉.柔性作业车间调度方法研究[D].武汉：华中科技大学,2009.
[张敬敏,2011]张敬敏,张有华,李霞.多目标柔性作业车间调度问题的混合差分算法[J].计算机工程与应用,2011,47(20)：245-248.
[张瑞,2008]张瑞,吴澄.求解大规模车间调度问题的一种分解优化算法[J].计算机集成制造系统,2008,14(8)：1559-1565.
[赵良辉,2009]赵良辉.作业车间调度问题的文化算法[J].计算机工程,2009,35(13)：196-198.
[赵楠,2012]赵楠,牛占文,郭伟.云制造环境中面向设计知识资源序列化组合的量子和声搜索算法[J].计算机集成制造系统,2012,18(7)：1435-1443.
[郑章元,2011]郑章元.蛙跳算法及其在置换流水车间调度中的应用研究[D].武汉：华中科技大学,2011.