多目标柔性作业车间调度方法研究
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摘要
制造业是国民经济的重要组成部分。实际生产中的优化问题通常是多属性的,一般是对多个目标的同时优化,而且各个目标之间通常是不一致的,比如产品质量和生产成本往往是两个互相冲突的目标。此外,加工时间、完工时间和产品交货期等通常都是不确定的参数,实际生产过程中还存在着一些不确定的干扰因素,比如机器故障、原材料延迟到达、紧急订单的插入或者订单取消等突发事件,人们迫切需要对实际生产中的车间调度问题进行深入和广泛的研究,以更好地指导生产。本文正是在这样的背景下,结合实际生产调度问题所面临的多目标、参数不确定和动态性,对多目标柔性作业车间调度问题及其扩展进行了研究,并取得了一些有意义的研究成果。
本文首先对单目标的柔性作业车间调度进行研究。将遗传算法“适者生存”进化准则融入禁忌搜索算法,将遗传算法的全局搜索能力和禁忌搜索算法的局部搜索能力相结合,提出了混合遗传禁忌搜索算法解决单目标的柔性作业车间调度问题。在遗传算法进化过程中,针对柔性作业车间调度问题的特点,设计了一种扩展的基于工序的编码及其产生活动调度的解码机制,并根据染色体的结构提出有效的交叉操作,IPOX交叉操作和一种新多点交叉操作MPX。对于遗传算法进化过程中产生的个体,应用禁忌搜索算法进行改进。将Balas和Vazacopoulos提出的邻域结构扩展到柔性作业车间调度问题,设计了禁忌搜索算法的邻域结构。运用提出的混合遗传禁忌搜索算法求解基准测试问题并与其他方法进行比较,验证了算法的有效性。
根据现实制造系统中关注较多的最大完工时间、平均流经时间、总拖期时间、机器总负荷、瓶颈机器负荷和生产成本等性能指标,建立了多目标柔性作业车间调度模型。在上述遗传算法的编码、交叉和变异等基本操作的基础上,提出了一种新的多目标遗传算法。在该多目标遗传算法里,采用了快速排序方法以提高算法构造Pareto最优解集的效率；采用了NSGA-Ⅱ的精英保留策略,并针对NSGA-Ⅱ在精英保留策略上的不足,引入了分布函数；此外,还采用了免疫和熵原理以维持进化种群的多样性。通过测试基准和模拟实际生产的算例,验证了算法的可行性和有效性；并利用层析分析法从一组Pareto最优解中选出最优妥协解。
针对实际制造车间调度中,加工时间和交货期不确定的特点,本文结合模糊集的相关理论,建立了多目标模糊柔性作业车间调度模型。基于上述多目标遗传算法,求解了具有模糊加工时间和模糊交货期的多目标柔性作业车间调度问题,并通过算例测试,验证了算法的有效性。
针对实际制造车间调度具有动态性的特点,提出了一种基于滚动窗口的动态调度优化策略,该策略采用基于周期和事件驱动的再调度机制将调度过程分成连续静态调度区间,在每个区间内用前面提出的多目标遗传算法进行优化调度。基于滚动窗口技术和多目标遗传算法,以交货期、最大流经时间、最大完工时间以及初始调度的偏离程度为性能指标进行同时优化。为了适应复杂多变的动态环境和保持生产的稳定性,提出了一种人机协同的调度机制。通过对算例进行测试,验证了该方法的可行性。
在算法研究的基础上开发了多目标柔性作业车间调度原型系统,对原型系统的功能和效果进行了描述。
最后,对全文所做的工作进行了总结,并对未来的研究方向进行了展望。
Manufacturing sector is an important part and the main force of national economy. However, several objectives must be considered simultaneously in the real-world production situation and these objectives often conflict with each other. For an enterprise, different departments have different expectations in order to maximize their own interests. Moreover, as the international competition becoming more and more intense and the growing of personalized product demand by the market, process time, completion time, delivery time and so on in the real production have been unable to describe as determined parameters as they were used to be. Meanwhile, there may be some dynamic events, such as machine malfunction, late arrival of raw materials, emergency insertion or cancel of order and so on. In this situation, people need to research on scheduling problems under uncertainty immediately and wildly to guide the real production, not only the theory but also its application. In this background, combined with the uncertain and dynamic optimization problems in the real plant and production, main research of this thesis is about multi-objective flexible job shop scheduling problem. And some meaningful conclusions and results are pointed out from this research.
The Flexible Job-shop Scheduling Problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP). This paper presents a hybrid genetic algorithm and tabu search (GATS) which incorporates the principle of "the survival of the fittest" from genetic algorithm (GA) into tabu search (TS) to solve the FJSP. According to the characteristics of the FJSP, an extended operation-based representation which simultaneously describes the sequence of operations and the assignment of operations to machines is applied to represent solution for GATS, and solutions are constructed using a procedure that generates active schedules. Two effective crossover and mutation operators are proposed to adapt to the chromosome structure. After individuals of GA are obtained, TS is applied to improve these solutions. The neighborhood structures of TS are designed by extending proposition proposed by Balas and Vazacopoulos to FJSP. The hybrid algorithm is tested on a set of standard instance taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.
An improved multi-objective evolutionary algorithm (MOGA) is proposed for solving the mulyi-objective FJSP. Firstly, the multi-objective FJSP optimization model is put forward, which the makespan, the mean flow-time, total tardiness, total workload of machines, workload of the critical machine and production cost widely concerned in complex manufacturing system are considered. In order to ensure convergence and the diversity of the solutions, an improved non-dominated sorting genetic Algorithm (NSGA-II) is proposed and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. The proposed MOGA is evaluated on some representative instances and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm. Finally, the analytic hierarchy process (AHP) approach is used to select the satisfied solution.
For the undetermined environment, fuzzy set is introduced to solve multi-objective FJSP with undetermined processing time and due date. Based on the improved MOGA proposed, we solved the multi-objective flexible job shop scheduling problem with fuzzy processing time and fuzzy due date. The computational results validate the effectiveness of the proposed algorithm.
MOGA based on rolling-horizon procedure was proposed to solve the multi-objective dynamic FJSP. In this procedure, periodic and event driven rescheduling strategies were employed and the dynamic scheduling problem was decomposed into a series of continual and static scheduling problems, then the improved MOGA was applied to optimize each of the static scheduling problems. According to the characteristics of the dynamic scheduling problem, the efficient decoding procedure and genetic operators were presented for the improved multi-objective genetic algorithm, and the objectives of rescheduling were to minimize the makespan, total tardiness, the mean flow-time, deviations from the pre-schedule. In order to adapt to the complex manufact uring environment and sustain the stability of production, a human-computer collaborative scheduling procedure was presented for the implementation of the scheduling process. The approach was tested on the instance, and the simulation results validate the effectiveness of the proposed strategies.
Real production workshop oriented scheduling prototype system is designed and developed based on the research findings above. The system architecture, development principles and function modules are described.
Finally, the research in the whole dissertation is summarized and future work is generalized and looked forward.
本文首先对单目标的柔性作业车间调度进行研究。将遗传算法“适者生存”进化准则融入禁忌搜索算法,将遗传算法的全局搜索能力和禁忌搜索算法的局部搜索能力相结合,提出了混合遗传禁忌搜索算法解决单目标的柔性作业车间调度问题。在遗传算法进化过程中,针对柔性作业车间调度问题的特点,设计了一种扩展的基于工序的编码及其产生活动调度的解码机制,并根据染色体的结构提出有效的交叉操作,IPOX交叉操作和一种新多点交叉操作MPX。对于遗传算法进化过程中产生的个体,应用禁忌搜索算法进行改进。将Balas和Vazacopoulos提出的邻域结构扩展到柔性作业车间调度问题,设计了禁忌搜索算法的邻域结构。运用提出的混合遗传禁忌搜索算法求解基准测试问题并与其他方法进行比较,验证了算法的有效性。
根据现实制造系统中关注较多的最大完工时间、平均流经时间、总拖期时间、机器总负荷、瓶颈机器负荷和生产成本等性能指标,建立了多目标柔性作业车间调度模型。在上述遗传算法的编码、交叉和变异等基本操作的基础上,提出了一种新的多目标遗传算法。在该多目标遗传算法里,采用了快速排序方法以提高算法构造Pareto最优解集的效率；采用了NSGA-Ⅱ的精英保留策略,并针对NSGA-Ⅱ在精英保留策略上的不足,引入了分布函数；此外,还采用了免疫和熵原理以维持进化种群的多样性。通过测试基准和模拟实际生产的算例,验证了算法的可行性和有效性；并利用层析分析法从一组Pareto最优解中选出最优妥协解。
针对实际制造车间调度中,加工时间和交货期不确定的特点,本文结合模糊集的相关理论,建立了多目标模糊柔性作业车间调度模型。基于上述多目标遗传算法,求解了具有模糊加工时间和模糊交货期的多目标柔性作业车间调度问题,并通过算例测试,验证了算法的有效性。
针对实际制造车间调度具有动态性的特点,提出了一种基于滚动窗口的动态调度优化策略,该策略采用基于周期和事件驱动的再调度机制将调度过程分成连续静态调度区间,在每个区间内用前面提出的多目标遗传算法进行优化调度。基于滚动窗口技术和多目标遗传算法,以交货期、最大流经时间、最大完工时间以及初始调度的偏离程度为性能指标进行同时优化。为了适应复杂多变的动态环境和保持生产的稳定性,提出了一种人机协同的调度机制。通过对算例进行测试,验证了该方法的可行性。
在算法研究的基础上开发了多目标柔性作业车间调度原型系统,对原型系统的功能和效果进行了描述。
最后,对全文所做的工作进行了总结,并对未来的研究方向进行了展望。
Manufacturing sector is an important part and the main force of national economy. However, several objectives must be considered simultaneously in the real-world production situation and these objectives often conflict with each other. For an enterprise, different departments have different expectations in order to maximize their own interests. Moreover, as the international competition becoming more and more intense and the growing of personalized product demand by the market, process time, completion time, delivery time and so on in the real production have been unable to describe as determined parameters as they were used to be. Meanwhile, there may be some dynamic events, such as machine malfunction, late arrival of raw materials, emergency insertion or cancel of order and so on. In this situation, people need to research on scheduling problems under uncertainty immediately and wildly to guide the real production, not only the theory but also its application. In this background, combined with the uncertain and dynamic optimization problems in the real plant and production, main research of this thesis is about multi-objective flexible job shop scheduling problem. And some meaningful conclusions and results are pointed out from this research.
The Flexible Job-shop Scheduling Problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP). This paper presents a hybrid genetic algorithm and tabu search (GATS) which incorporates the principle of "the survival of the fittest" from genetic algorithm (GA) into tabu search (TS) to solve the FJSP. According to the characteristics of the FJSP, an extended operation-based representation which simultaneously describes the sequence of operations and the assignment of operations to machines is applied to represent solution for GATS, and solutions are constructed using a procedure that generates active schedules. Two effective crossover and mutation operators are proposed to adapt to the chromosome structure. After individuals of GA are obtained, TS is applied to improve these solutions. The neighborhood structures of TS are designed by extending proposition proposed by Balas and Vazacopoulos to FJSP. The hybrid algorithm is tested on a set of standard instance taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.
An improved multi-objective evolutionary algorithm (MOGA) is proposed for solving the mulyi-objective FJSP. Firstly, the multi-objective FJSP optimization model is put forward, which the makespan, the mean flow-time, total tardiness, total workload of machines, workload of the critical machine and production cost widely concerned in complex manufacturing system are considered. In order to ensure convergence and the diversity of the solutions, an improved non-dominated sorting genetic Algorithm (NSGA-II) is proposed and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. The proposed MOGA is evaluated on some representative instances and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm. Finally, the analytic hierarchy process (AHP) approach is used to select the satisfied solution.
For the undetermined environment, fuzzy set is introduced to solve multi-objective FJSP with undetermined processing time and due date. Based on the improved MOGA proposed, we solved the multi-objective flexible job shop scheduling problem with fuzzy processing time and fuzzy due date. The computational results validate the effectiveness of the proposed algorithm.
MOGA based on rolling-horizon procedure was proposed to solve the multi-objective dynamic FJSP. In this procedure, periodic and event driven rescheduling strategies were employed and the dynamic scheduling problem was decomposed into a series of continual and static scheduling problems, then the improved MOGA was applied to optimize each of the static scheduling problems. According to the characteristics of the dynamic scheduling problem, the efficient decoding procedure and genetic operators were presented for the improved multi-objective genetic algorithm, and the objectives of rescheduling were to minimize the makespan, total tardiness, the mean flow-time, deviations from the pre-schedule. In order to adapt to the complex manufact uring environment and sustain the stability of production, a human-computer collaborative scheduling procedure was presented for the implementation of the scheduling process. The approach was tested on the instance, and the simulation results validate the effectiveness of the proposed strategies.
Real production workshop oriented scheduling prototype system is designed and developed based on the research findings above. The system architecture, development principles and function modules are described.
Finally, the research in the whole dissertation is summarized and future work is generalized and looked forward.
引文
[1]陈荣秋,马士华.生产与运作管理.北京：高等教育出版社,1999
[2]Pinedo M. Scheduling theory, algorithms, and system.2nd, Prentice Hall, Upper Saddle River, New Jersey,2002
[3]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.博士学位论文,华中科技大学,2006
[4]Garey E L, Johnson D S, Sethi R. The complexity of flowshop and job-shop scheduling. Mathematics of Operations Research,1976,1:117-129
[5]Holand J H. Adaptation in natural and artificial systems. Ann Arbor:The University of Michigan Press,1975
[6]王凌.车间调度及其遗传算法.北京：清华大学出版社,2003
[7]Kennedy J and Eberhart R C. Particle swarm optimization, in:Proceedings of IEEE International Conference on Neutral Networks, Perth, Australia,1995,1942-1948
[8]Eberhart R C and Kennedy J. A new optimizer using particle swarm theory, in: Proceedings of Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan,1995,39-43
[9]Dorigo M and Di Caro G. Ant colony optimization:a new meta-heuristic. in: Proceedings of the 1999 Congress on Evolutionary Computation,1999,1470-1477
[10]Glover F. Future paths for integer programming and links to artificial intelligence. Computers and Operations Research,1986,13:533-549
[11]Hansen P. The steepest ascent mildest descent heuristic for combinatorial programming, in:Congress on Numerical Methods in Combinatorial Optimization, Capri, Italy,1986.
[12]Kirkpatrick S. Gelatt C D and Vecchi M P. Optimization by simulated annealing. Science.1983,13 May,220(4598):671-680
[13]Adleman L. Molecular computation of solution to combinatorial problems. Science, 1994,66(11):1021-1024
[14]Blazewicz J, Domschke W, Pesch E. The job shop scheduling problem:conventional and new solution techniques. European Journal of Operational Research,1996,93: 1-33
[15]Jain A S, Meeran S. Deterministic job shop scheduling:past, present and future. European Journal of Operational Research,1999,113:390-434
[16]王凌.智能优化算法及其应用.北京：清华大学出版社,2001
[17]刑文训,谢金星.现代优化计算方法.北京：清华大学出版社,2001
[18]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.博士学位论文,中国科学技术大学,2008
[19]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
[20]Davis Lawrence. Job shop scheduling with genetic algorithms [C].In:Proceedings of the 1st International Conference on Genetic Algorithms and their Applications,1985,pp.136-140
[21]Bucker P. and Schlie R. Job-shop scheduling with multi-purpose machines[J].Computing,1990,45:369-375
[22]姜思杰.基于GA和TS的最小平衡算法及其在FMS中的应用.博士学位论文,哈尔滨工业大学,1999
[23]Dauzere-Peres S., Roux W. and Lasserre J.B. Multi-resource shop scheduling with resource flexiblility[J]. European Journal of Operation Research,1998,107(2):289-305
[24]Angel E., Bampis E. and Gourves L. Approximation results for a bicriteria job scheduling problem on a single machine without preemption[J]. Information Processing Letters,2005,94(1):19-27
[25]Alvarez V.R., Fuertes A., Tamarit J.M. et al. A heuristic to schedule flexible job shop in a glass factory[J]. European Journal of Operation Reaearch,2005,165(2):525-534
[26]潘全科.智能制造系统多目标车间调度研究.博士学位论文,南京航空航天大学,2003
[27]Chen H., Ihlow J. and Lehmann C. A genetic algorithm for flexible job-shop scheduling[C].In:Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit Michigan:IEEE,1999,2:1120-1125
[28]Ho N.B. and Tay J.C. GENACE:an efficient culture algorithm solving the flexible job shop problem [C]. In:Proceedings of Congress on Evolutionary Computation,2004,pp.1759-1766
[29]Ho N.B. and Tay J.C. LEGA:an architecture for learning and evolutionary flexible job-shop schedules [C]. The 2005 IEEE Congress on Evolutionary Computation,2005,2(2-5):1380-1387
[30]Najid N.M., Dauzere-Peres S. and Zaidat A. A modified simulated annealing method for flexible job shop scheduling problem [C]. In:Proceedings of the IEEE International Conference on Systems Man and Cybernetics. NJ,USA,IEEE,2002,pp.89-94
[31]Mohammad S.M. and Parviz F. Flexible job shop scheduling with tabu search algorithms [J]. International Journal of Advanced Manufacturing Technology,2006,32(5-6):563-570
[32]Kacem I. Genetic algorithm for the flexible job shop scheduling problem[C].IEEE International Conference on Systems, Man and Cybernetics,2003,4:3464-3469
[33]Mastrolilli M, Gambardella LM (2000) Effective neighborhood functions for the flexible job shop problem. J Scheduling 3(1):3-20
[34]Zribi N. and Kacem I. et al. Minimizing the total tardiness in a flexible job-shop.2005(http://www.wacong.org/wac2006/allpapers/isiac/isiac_45.pdf)
[35]吴秀丽.多目标柔性作业车间调度技术研究.博士学位论文,西北工业大学,2006
[36]卢开澄.单目标、多目标与整数规划[M].北京：清华大学出版社,1999：13-13
[37]Rigao C. Tardiness Minimization in a Flexible Job Shop:a Tabu Search Approach[J] Journal of Intelligent Manufacturing,2004,15(1):103-115
[38]Deb K. Non-linear Goal Programming Using Multi-Objective Genetic Algorithms [R]. Technical Report No CI-60/98, Department of Computer Science/XI, University of Dortmund, Germany,1999
[39]Loukil T., Teghem J. and Tuyttens D. Solving Multi-objective Production Scheduling Problems Using Metsheuristics [J]. European Journal of Operation Research,2005,161(1):42-61
[40]Kacem I., Hammadi S. and Borne P. Pareto-optimality Approach for Flexible Job-shop Scheduling Problems:Hybridization of Evolutionary Algorithms and Fuzzy Logic [J].Mathematics and Computers in Simulation,2002,60(3-5):245-276
[41]Narula S.C. and Vassilev V. An Interactive Algorithm for Solving Multiple Objective Integer Linear Programming Problems [J]. European Journal of Operation Research, 1994,79(3):443-450
[42]Angel E., Bampis E. and Gourves L. Approximation Results for a Bicriteria Job Scheduling Problem on a Single Machine without Preemption [J]. Information Processing Letters,2005,94(1):19-27
[43]Torabi S.A., Karimi B. and Fatemi S.M.T. The Common Cycle Economic Lot Scheduling in Flexible Job Shops:The Finite Horizon Case [J]. International Journal of Production Economics,2005,97(1):52-65
[44]Hoitomt D.J. A Practical Approach to Job-Shop Scheduling Problems [C]. The Proceedings of IEEE Transations on Robotics and Automation, Piscataway:IEEE Press,1993,9(1):1-13
[45]Tamaki H. Modeling and Genetic Solution of a Class of Flexible Job Shop Scheduling Problems [C]. The Proceedings of 20018th IEEE International Conference on Emerging Technologies and Factory Automation, Piscataway:IEEE Press, 2000,343-350
[46]Alvarez-Valdes R., Fuertes A. and Tamarit J.M. et al. A Heuristic to Schedule Flexible Job-shop in a Glass Factory [J]. European Journal of Operational Research, 2005,165(2):525-534
[47]Zhang Haipeng, Gen M. Multistage-based genetic algorithm for flexible job shop scheduling problems [C]. In Proceedings of the 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems,2004,pp.223-232
[48]Cochran K.J., Horngs M. and Fowler W.J. A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines [J]. Computers & Operations Research,2003,30(7):1087-1102
[49]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].控制与系统,2005,20(2)：137-141
[50]Vicent T. Solving a bicriteria scheduling problem on unrelated parallel machines occurring in the glass bottle industry [J]. European Journal of Operation Research, 2001,135(1):42-49
[51]Vicent T., Nicolas M. and Fabrice T. An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem [J]. European Journal of Operational Research,2002,142(2):250-257
[52]Vicent T., Billaut J.C. Multicriteria scheduling:theory, models and algorithms [M].Springer-Verlag,2002
[53]李富明,朱云龙,尹朝万,宋晓宇.基于模糊遗传算法的模糊调度研究.信息与控制,2004,33(6)：703-708
[54]朱云龙,于海斌.面向客户的随机动态生产管理模式研究[J].计算机集成制造系统,2001,7(3)：14-18.
[55]刘茂盛.基于遗传算法的柔性作业车间模糊调度的研究.硕士学位论文,内蒙古大学,2009
[56]李春廷.基于遗传算法的作业车间调度问题研究.燕山大学硕士论文,2007
[57]Ishii H,Tada M,Masuda T. Two scheduling problemswith fuzzy due-dates [J]. Fuzzy Sets and Systems,1992,46(3):339-347.
[58]Wang CY,WangDW,IpWH,et al. The single machine ready time scheduling problemwith fuzzy processing times [J]. Fuzzy Sets and Systems,2002,127(2):117-129.
[59]Peng J,Liu B D. Parallel machine scheduling models with fuzzy processing times [J]. Information Sciences,2004,164(1):1-18.
[60]Han S,Ishii H,Fujii S. One machine scheduling problem with fuzzy due dates [J]. European Journal of Operational Research,1994,49(1):1-12.
[61]Ishibuchi H,Yamamoto N,Murata T,et al. Genetic algorithms and neighborhood search algorithm for fuzzy flowshop scheduling problem[J]. Fuzzy Sets and Systems,1994,67(1):81-100.
[62]Ishibuchi H,Murata T,Lee K H. Formulation of fuzzy flowshop scheduling problems with fuzzy processing time [A]. Proceedings of the 5th IEEE International Conference on Fuzzy Systems [C]. New York, NY, USA:IEEE,1996.199-205.
[63]Tsujimura Y,Gen M,Kubota E. Solving job-shop scheduling problems with fuzzy processing time using genetic algorithms [J]. Journal of Japan Society for Fuzzy Theory and Systems,1995,7(2):1073-1083.
[64]王玮,汪定伟.单件制造业模糊交货期下的准时化生产计划[J].系统工程理论与实践,1997,17(11)：74-78.
[65]Murata T,Gen M,Ishibuchi H. Multi-objective scheduling with fuzzy due-date [J]. Computers& Industrial Engineerig,1998,35(3):439-442.
[66]Adamopoulos G I,Pappis C P. A neighbourhood-based hybrid method for scheduling with fuzzy due-dates [J]. International Transactions in Operational Research,1998,5(3):147-153.
[67]Lam S S,Cai S. Single machine scheduling with nonlinear lateness cost functions and fuzzy due dates [J]. Nonlinear Analysis:Real World Applications,2002,3(2):307-316.
[68]吴 悦,汪定伟.用模拟退火法解任务的加工时间为模糊区间数的单机提前／拖期调度问题[J].信息与控制,1998,27(5)：394-400.
[69]王成尧,高 麟,汪定伟.模糊加工时间调度问题的研究[J].系统工程学报,1999,14(3)：238-242.
[70]唐国春,张 峰,罗守成.模糊加工时间排序问题的性质[J].运筹学报,1999,3(4)：84-94.
[71]王成尧,汪定伟.单机模糊加工时间下最迟开工时间调度问题[J].控制与决策,2000,15(1)：71-74.
[72]Lin FT. A job-shop scheduling problem with fuzzy processing times [A]. Proceedings of International Conference on Computational Science Part Ⅱ [C]. Berlin Heidelberg: Springer,2001.409-418.
[73]胡冬华.基于CPM和TOC的单件小批生产计划与调度方法研究.硕士学位论文,南京航空航天大学,2009
[74]郑晓伟.基于遗传算法的作业车间动态调度研究.硕士学位论文,武汉理工大学,2008
[75]聂书志.基于DNA遗传算法的协同制造资源优化配置技术研究.博士学位论文,华南理工大学,2010
[76]Sakawa M,Mori T. An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate [J]. Computers&Industrial Engineering,1999,36(2):325-341.
[77]ItohT,Ishi H. Fuzzy due-date scheduling problem with fuzzy processing time [J]. International Transactions in Operational Research,1999,6(6):639-647.
[78]Sakawa M,Kubota R. Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms [J]. European Journal of Operational Research,2000,120(2):393-407.
[79]Chanas S,Kasperski A. Minimizingmaximum lateness in a single machine scheduling problem with fuzzy processing times and fuzzy due dates [J]. Engineering Applications of Artificial Intelligence,2001,14(3):377-386.
[80]Chanas S,Kasperski A. On two single machine scheduling problems with fuzzy processing times and fuzzy due dates [J]. European Journal of Operational Research,2003,147(2):281-296.
[81]耿兆强,邹益仁.用遗传算法解决一类模糊流水车间调度问题[J].系统工程与电子技术,2002,24(16)：5-7.
[82]耿兆强,邹益仁.基于遗传算法的作业车间模糊调度问题的研究[J].计算机集成制造系统,2002,8(8)：616～620.
[83]万国华.用模糊调度系统求解动态Job Shop问题.系统工程理论与实践,2001,(8)：97-101
[84]吴秀丽.柔性作业车间动态调度问题研究.系统仿真学报,2008,20(14)：3828-3832
[85]Jackson J R. Simulation research on job shop production. Naval Res Log Quart,1957, 4(3):287-295
[86]Lawler E L, Lenstra J K, Rinnooy Kan A H G. Recent developments in deterministic sequencing and scheduling:a survey. In:Dempster M A H et al. (Eds.), Deterministic and Stochastic Scheduling. Reidel, Dordrecht,1982,35-73
[87]潘全科,朱剑英.作业车间动态调度研究.南京航空航天大学学报,2005,37(2)：262-268
[88]王锐.应用多种群遗传算法求解动态车间调度问题.硕士学位论文,大连理工大学,2009
[89]E.L. Garey, D.S.Johnson and R.Sethi, "The complexity of flowshop and job-shop scheduling," Mathematics of Operations Research, vol.1, pp.117-129,1976.
[90]M. Montazeri and L.N. Van Wassenhove, "Analyses of scheduling rules for an FMS,' Int. J. Prod. Res., vol.28, pp.785-802,1990.
[91]H. Chen, J. Ihlow and C. Lehmann, "A Genetic Algorithm for Flexible Job-Shop Scheduling," in IEEE International Conference on Robotics and Automation, Detroit, 1999, pp.1120-1125.
[92]M. Mastrolilli and L.M. Gambardella, "Effective neighbour-hood functions for the flexible job shop problem," Journal of Scheduling, vol.3, no.1, pp.3-20,2000.
[93]J.W. Barnes and J.B. Chambers, "Flexible Job Shop Scheduling by Tabu Search, Graduate Program in Operations Research and Industrial Engineering," Tech. Rep. ORP 9609, University of Texas, Austin,1996.
[94]N.M. Najid, S. Dauzere-Pere and A. Zaidat, "A modified simulated annealing method for Flexible Job Shop Scheduling Problem," in Proceedings of the 2002 International Conference on Systems, Man and Cybernetics. Piscataway, NJ:IEEE Press,2002, pp. 6-12.
[95]P. Brandimarte, "Routing and scheduling in a flexible job shop by tabu search," Annals of Operations Research, vol.41, pp.157-183,1993.
[96]E. Balas, "Machine scheduling via disjunctive graphs:an implicit enumeration algorithm," Operations Research, vol.17, pp.941-957,1969.
[97]张超勇；董星；王晓娟；李新宇；刘琼.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.机械工程学报,2010,46(11)：156-164
[98]张超勇,饶运清,李培根,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.
[99]Zhang CY, Li PG, Rao YQ, Li SX (2005) A new hybrid GA/SA algorithm for the job shop scheduling problem. Lecture Notes in Computer Science 3448:246-259
[100]E. Balas and A. Vazacopoulos, "Guided local search with shifting bottleneck for job shop scheduling," Management Science, vol.44, no.2, pp.262-275,1998.
[101]吴秀丽,孙树栋,杨展等.多目标柔性Job Shop调度问题的技术现状和发展趋势[J].计算机应用研究,2007.3,24(3)：1-5,9.
[102]Garey E L, Johnson D S, Sethi R. The complexity of flowshop and job-shop scheduling [J]. Mathematics of Operations Research,1976,1:117-129.
[103]李新宇.工艺规划与车间调度集成问题的求解方法研究.博士学位论文,华中科技大学,2009
[104]郑金华.多目标进化算法及其应用.北京：科学出版社.2007
[105]刘兰霞.多目标粒子群优化算法研究.硕士学位论文,湖南科技大学,2010
[106]张国辉.柔性作业车间调度方法研究.博士学位论文,华中科技大学,2009
[107]刘捷.不确定条件下基于遗传算法的柔性作业车间调度问题研究.硕士学位论 文,华中科技大学,2009
[108]D.E.Goldberg,Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA:Addison-Wesley,1989
[109]栾威.基于多目标遗传算法求解Steiner树问题.硕士学位论文,东北大学,2008
[110]张武华.SB公司多工厂生产协调优化问题研究.硕士学位论文,华南理工大学,2010
[111]Horn J, Nafpliotis N, Goldberg D E. A niched pareto genetic algorithm for multiobjective optimization. Proceedings of the first IEEE Conference on Evolutionary Computation, Piscataway USA,1994,82-87
[112]Srinivas N, Deb K. Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation,1994,2(3):221-248
[113]Zitzler E, Thiele L. Multiobjective evolutionary algorithms:a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation,1999,3(4):257-271
[114]Zitzler E, Laumanns M, Thiele L. SPEA2:improving the strength pareto evolutionary algorithm, TIK-Report 103,2001
[115]Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm NSGA-II. IEEE Transaction on Evolutionary Computation,2002, 6(2):182-197
[116]Zeng Sanyou, Kang Lishan, Ding Lixin. An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints Evolutionary Computation. MIT Press,2004
[117]郑金华,史忠植,谢勇.基于聚类的快速多目标遗传算法.计算机研究与发展,2004,41(7)：1081-1087
[118]郭秀萍.多目标进化算法及其在制造系统中的应用研究.博士学位论文,上海交通大学,2007
[119]张超勇,刘琼,邱浩波,邵新宇.考虑加工成本和时间的柔性作业车间调度问题研究.机械科学与技术,2009：28(8)：1005-1011
[120]Xia W J, Wu Z M. An effective hybrid optimization approach for multi objective flexible job shop scheduling problems. Computers & Industrial Engineering,2005, 48(2):409-425
[121]余建军,孙树栋,郝京辉.免疫算法求解多目标柔性作业车间调度研究.计算机集成制造系统,2006,12(10)：1643-1650
[122]Mahfoud SW (1995) Niching methods for genetic algorithms. Dissertation, University of Illinois at Urbana-Champaign
[123]Cui XX, Li M, Fang TJ (2001) Study of population diversity of multiobjective evolutionary algorithm based on immune and entropy principles. Proceedings of the 2001 Congress on Evolutionary Computation.Seoul:OPAC. pp 1316-1321
[124]Shimooka T, Shimizu K (2004) Artificial Immune System for Personal Identification with Finger Vein Pattern. Lecture Notes in Computer Science 3214: 511-518
[125]Xiao RB, Cao PB, Liu Y (2007) Engineering Immune Computing. Science Press, Beijing, China. In Chinese
[126]彭琰.基于生物激励机制的算法研究.硕士学位论文,湘潭大学,2007
[127]赵君莉,杨善学,王宇平.改进的非支配排序遗传算法INSGA-II西安科技大学学报,2006,26(4)：34-35
[128]Kacem I, Hammadi S, Borne P (2002) Pareto-optimality approach for flexible job-shop scheduling problems:hybridization of evolutionary algorithms and fuzzy logic. Math Comput Simul 60(3-5):245-276[130] Brandimarte P (1993) Routing and scheduling in a flexible job shop by taboo search. Ann Oper Res 41:157-183
[129]Xia WJ, Wu ZM (2005) An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 48 (2): 409-425
[130]Zhang GH, Shao XY, Li PL, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309-1318
[131]Xing LN, Chen YW, Yang KW (2009) An efficient search method for multi-objective flexible job shop scheduling problems. J Intell Manuf 20:283-293
[132]Thomas L. Saaty. An Exposition on the AHP in Reply to the Paper "Remarks on the Analytic Hierarchy Process".Management sience,1990,36(3):259-268
[133]Omar A.ghrayeb, A bi-criteria optimization:Minimizing the integral value and spread of the fuzzy makespan of job shop scheduling problems [J] Applied soft computing 2/3F (2003)197～210
[134]Tadahiko Murata, Hisao Ishibuchi, Mtsuo Gen, Multi-Objective Fuzzy Scheduling with the OWA Operator for Handling Different Scheduling Criteria and Different Job Importance [C], IEEE International Fuzzy systems conference Proceedings, August 22-25,1999, Seoul, Korea
[135]Masatoshi sakawa, Ryo Kubota. Fuzzy programming for multi-objective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithm. European Journal of Operational Research,2000,120(2):393～407
[136]Imed Kacem, Slim Hammadi and Pierre Borne. Pareto-optimality approach for flexible job-shop scheduling problems:hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation,2002,60(3-5):245-276
[137]Dobrila Petrovic, Alejandra Duenas and Sanja Petrovic. Decision Support Systems,2007,43(4):1527-1538
[138]Fu-ming Li, Yun-long Zhu, Chao-wan Yin et al. Fuzzy Programming for Multiobjective Fuzzy JobShop Scheduling with Alternative MachinesThrough Genetic Algorithms. L. Wang, K. Chen, and Y.S. Ong (Eds.):ICNC 2005, LNCS 3611, pp.992-1004,2005
[139]Deming Lei. Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems. International Journal of Advanced Manufacturing and Technology,2008,37:157-165
[140]阚震.基于遗传算法的作业车间多目标模糊调度问题研究.硕士学位论文,合肥工业大学,2007
[141]崔瑞娟.模糊项目调度优化算法研究.硕士学位论文,哈尔滨理工大学,2007
[142]程蓉.复杂生产环境下优化调度方法研究与系统实现.博士学位论文,华中科技大学,2006
[143]孙中华.基于颜色与目标轮廓特征的视频分割方法.硕士学位论文,吉林大学,2004
[144]张松顺.半监督学习方法及其应用研究.硕士学位论文,江南大学,2009
[145]宋璐.流程工业的不确定性生产调度研究.硕士学位论文,浙江工业大学,2006
[146]高原.基于浏览行为的用户兴趣量度.硕士学位论文,重庆大学,2006
[147]鲁燕飞.基于Rough集理论的风险决策评价方法研究和应用.硕士学位论文,南昌大学,2006
[148]雷德明,吴智铭.多目标模糊作业车间调度问题研究.计算机集成制造系统,2006,12(2)：174-179
[149]Masatoshi sakawa, Ryo Kubota. Fuzzy programming for multi-objective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithm. European Journal of Operational Research,2000,120(2):393-407
[150]J ackson J R. Simulation Research on Job Shop Production[J]. Naval Res. Log. Quart.,1957,4 (3):287-295.
[151]潘全科,朱剑英.作业车间动态调度研究[J].南京航空航天大学学报,2005,37(2)：262-268.
[152]Nelson R T, Holloway C A, Wong R M. Centralized Scheduling and Priority Implementation Heuristics for a Dynamic Job Shop Model with due Dates and Variable Processing Time [J]. AIIE Transactions,1977,19:96-102.
[153]Coello C A C. Evolutionary Multi-objective Optimization:a Historical View of the Field[J]. Computational Intelligence Magazine,2006,1 (1):28-36.
[154]Abumaizar R J, Svestka J A. Rescheduling Job Shops under Random Disruptions [J]. Int. J. Prod.Res.,1997,35:2065-2082.
[155]Raheja A S, Subramaniam V. Reactive Recovery of Job Shop Schedules-a Review [J]. Int. J. Adv.Manuf. Technol.,2002,19:756-763.
[156]连坤雷,张超勇,高亮,张朝阳.一种基于改进遗传算法的多目标动态调度优化.中国制造业信息化,2010,2：13-17
[157]Wong T N,Leung C W,Mak K L,et al. Integrated Process Planning and Scheduling/Rescheduling-an Agent-based Approach[J]. International Journal of Production Research,2006,44 (18/19):3627-3655.
[158]张超勇,李新宇,王晓娟,刘琼,高亮.基于滚动窗口的多目标动态调度优化研究,中国机械工程,2009,20(18)：2190-2197
[159]Raman N, Talbot F. The Job Shop Tardiness Problem:a Decomposition Approach [J]. European Journal of Operational Research,1993,69:187-199.
[160]Sabuncuoglu I,Karabuk S. Rescheduling Frequency in an FMS with Uncertainy Processing Times and Unreliable Machines [J]. Journal of Manufacturing Systems,1999,18 (4):268-281.
[2]Pinedo M. Scheduling theory, algorithms, and system.2nd, Prentice Hall, Upper Saddle River, New Jersey,2002
[3]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.博士学位论文,华中科技大学,2006
[4]Garey E L, Johnson D S, Sethi R. The complexity of flowshop and job-shop scheduling. Mathematics of Operations Research,1976,1:117-129
[5]Holand J H. Adaptation in natural and artificial systems. Ann Arbor:The University of Michigan Press,1975
[6]王凌.车间调度及其遗传算法.北京：清华大学出版社,2003
[7]Kennedy J and Eberhart R C. Particle swarm optimization, in:Proceedings of IEEE International Conference on Neutral Networks, Perth, Australia,1995,1942-1948
[8]Eberhart R C and Kennedy J. A new optimizer using particle swarm theory, in: Proceedings of Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan,1995,39-43
[9]Dorigo M and Di Caro G. Ant colony optimization:a new meta-heuristic. in: Proceedings of the 1999 Congress on Evolutionary Computation,1999,1470-1477
[10]Glover F. Future paths for integer programming and links to artificial intelligence. Computers and Operations Research,1986,13:533-549
[11]Hansen P. The steepest ascent mildest descent heuristic for combinatorial programming, in:Congress on Numerical Methods in Combinatorial Optimization, Capri, Italy,1986.
[12]Kirkpatrick S. Gelatt C D and Vecchi M P. Optimization by simulated annealing. Science.1983,13 May,220(4598):671-680
[13]Adleman L. Molecular computation of solution to combinatorial problems. Science, 1994,66(11):1021-1024
[14]Blazewicz J, Domschke W, Pesch E. The job shop scheduling problem:conventional and new solution techniques. European Journal of Operational Research,1996,93: 1-33
[15]Jain A S, Meeran S. Deterministic job shop scheduling:past, present and future. European Journal of Operational Research,1999,113:390-434
[16]王凌.智能优化算法及其应用.北京：清华大学出版社,2001
[17]刑文训,谢金星.现代优化计算方法.北京：清华大学出版社,2001
[18]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.博士学位论文,中国科学技术大学,2008
[19]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
[20]Davis Lawrence. Job shop scheduling with genetic algorithms [C].In:Proceedings of the 1st International Conference on Genetic Algorithms and their Applications,1985,pp.136-140
[21]Bucker P. and Schlie R. Job-shop scheduling with multi-purpose machines[J].Computing,1990,45:369-375
[22]姜思杰.基于GA和TS的最小平衡算法及其在FMS中的应用.博士学位论文,哈尔滨工业大学,1999
[23]Dauzere-Peres S., Roux W. and Lasserre J.B. Multi-resource shop scheduling with resource flexiblility[J]. European Journal of Operation Research,1998,107(2):289-305
[24]Angel E., Bampis E. and Gourves L. Approximation results for a bicriteria job scheduling problem on a single machine without preemption[J]. Information Processing Letters,2005,94(1):19-27
[25]Alvarez V.R., Fuertes A., Tamarit J.M. et al. A heuristic to schedule flexible job shop in a glass factory[J]. European Journal of Operation Reaearch,2005,165(2):525-534
[26]潘全科.智能制造系统多目标车间调度研究.博士学位论文,南京航空航天大学,2003
[27]Chen H., Ihlow J. and Lehmann C. A genetic algorithm for flexible job-shop scheduling[C].In:Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit Michigan:IEEE,1999,2:1120-1125
[28]Ho N.B. and Tay J.C. GENACE:an efficient culture algorithm solving the flexible job shop problem [C]. In:Proceedings of Congress on Evolutionary Computation,2004,pp.1759-1766
[29]Ho N.B. and Tay J.C. LEGA:an architecture for learning and evolutionary flexible job-shop schedules [C]. The 2005 IEEE Congress on Evolutionary Computation,2005,2(2-5):1380-1387
[30]Najid N.M., Dauzere-Peres S. and Zaidat A. A modified simulated annealing method for flexible job shop scheduling problem [C]. In:Proceedings of the IEEE International Conference on Systems Man and Cybernetics. NJ,USA,IEEE,2002,pp.89-94
[31]Mohammad S.M. and Parviz F. Flexible job shop scheduling with tabu search algorithms [J]. International Journal of Advanced Manufacturing Technology,2006,32(5-6):563-570
[32]Kacem I. Genetic algorithm for the flexible job shop scheduling problem[C].IEEE International Conference on Systems, Man and Cybernetics,2003,4:3464-3469
[33]Mastrolilli M, Gambardella LM (2000) Effective neighborhood functions for the flexible job shop problem. J Scheduling 3(1):3-20
[34]Zribi N. and Kacem I. et al. Minimizing the total tardiness in a flexible job-shop.2005(http://www.wacong.org/wac2006/allpapers/isiac/isiac_45.pdf)
[35]吴秀丽.多目标柔性作业车间调度技术研究.博士学位论文,西北工业大学,2006
[36]卢开澄.单目标、多目标与整数规划[M].北京：清华大学出版社,1999：13-13
[37]Rigao C. Tardiness Minimization in a Flexible Job Shop:a Tabu Search Approach[J] Journal of Intelligent Manufacturing,2004,15(1):103-115
[38]Deb K. Non-linear Goal Programming Using Multi-Objective Genetic Algorithms [R]. Technical Report No CI-60/98, Department of Computer Science/XI, University of Dortmund, Germany,1999
[39]Loukil T., Teghem J. and Tuyttens D. Solving Multi-objective Production Scheduling Problems Using Metsheuristics [J]. European Journal of Operation Research,2005,161(1):42-61
[40]Kacem I., Hammadi S. and Borne P. Pareto-optimality Approach for Flexible Job-shop Scheduling Problems:Hybridization of Evolutionary Algorithms and Fuzzy Logic [J].Mathematics and Computers in Simulation,2002,60(3-5):245-276
[41]Narula S.C. and Vassilev V. An Interactive Algorithm for Solving Multiple Objective Integer Linear Programming Problems [J]. European Journal of Operation Research, 1994,79(3):443-450
[42]Angel E., Bampis E. and Gourves L. Approximation Results for a Bicriteria Job Scheduling Problem on a Single Machine without Preemption [J]. Information Processing Letters,2005,94(1):19-27
[43]Torabi S.A., Karimi B. and Fatemi S.M.T. The Common Cycle Economic Lot Scheduling in Flexible Job Shops:The Finite Horizon Case [J]. International Journal of Production Economics,2005,97(1):52-65
[44]Hoitomt D.J. A Practical Approach to Job-Shop Scheduling Problems [C]. The Proceedings of IEEE Transations on Robotics and Automation, Piscataway:IEEE Press,1993,9(1):1-13
[45]Tamaki H. Modeling and Genetic Solution of a Class of Flexible Job Shop Scheduling Problems [C]. The Proceedings of 20018th IEEE International Conference on Emerging Technologies and Factory Automation, Piscataway:IEEE Press, 2000,343-350
[46]Alvarez-Valdes R., Fuertes A. and Tamarit J.M. et al. A Heuristic to Schedule Flexible Job-shop in a Glass Factory [J]. European Journal of Operational Research, 2005,165(2):525-534
[47]Zhang Haipeng, Gen M. Multistage-based genetic algorithm for flexible job shop scheduling problems [C]. In Proceedings of the 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems,2004,pp.223-232
[48]Cochran K.J., Horngs M. and Fowler W.J. A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines [J]. Computers & Operations Research,2003,30(7):1087-1102
[49]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].控制与系统,2005,20(2)：137-141
[50]Vicent T. Solving a bicriteria scheduling problem on unrelated parallel machines occurring in the glass bottle industry [J]. European Journal of Operation Research, 2001,135(1):42-49
[51]Vicent T., Nicolas M. and Fabrice T. An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem [J]. European Journal of Operational Research,2002,142(2):250-257
[52]Vicent T., Billaut J.C. Multicriteria scheduling:theory, models and algorithms [M].Springer-Verlag,2002
[53]李富明,朱云龙,尹朝万,宋晓宇.基于模糊遗传算法的模糊调度研究.信息与控制,2004,33(6)：703-708
[54]朱云龙,于海斌.面向客户的随机动态生产管理模式研究[J].计算机集成制造系统,2001,7(3)：14-18.
[55]刘茂盛.基于遗传算法的柔性作业车间模糊调度的研究.硕士学位论文,内蒙古大学,2009
[56]李春廷.基于遗传算法的作业车间调度问题研究.燕山大学硕士论文,2007
[57]Ishii H,Tada M,Masuda T. Two scheduling problemswith fuzzy due-dates [J]. Fuzzy Sets and Systems,1992,46(3):339-347.
[58]Wang CY,WangDW,IpWH,et al. The single machine ready time scheduling problemwith fuzzy processing times [J]. Fuzzy Sets and Systems,2002,127(2):117-129.
[59]Peng J,Liu B D. Parallel machine scheduling models with fuzzy processing times [J]. Information Sciences,2004,164(1):1-18.
[60]Han S,Ishii H,Fujii S. One machine scheduling problem with fuzzy due dates [J]. European Journal of Operational Research,1994,49(1):1-12.
[61]Ishibuchi H,Yamamoto N,Murata T,et al. Genetic algorithms and neighborhood search algorithm for fuzzy flowshop scheduling problem[J]. Fuzzy Sets and Systems,1994,67(1):81-100.
[62]Ishibuchi H,Murata T,Lee K H. Formulation of fuzzy flowshop scheduling problems with fuzzy processing time [A]. Proceedings of the 5th IEEE International Conference on Fuzzy Systems [C]. New York, NY, USA:IEEE,1996.199-205.
[63]Tsujimura Y,Gen M,Kubota E. Solving job-shop scheduling problems with fuzzy processing time using genetic algorithms [J]. Journal of Japan Society for Fuzzy Theory and Systems,1995,7(2):1073-1083.
[64]王玮,汪定伟.单件制造业模糊交货期下的准时化生产计划[J].系统工程理论与实践,1997,17(11)：74-78.
[65]Murata T,Gen M,Ishibuchi H. Multi-objective scheduling with fuzzy due-date [J]. Computers& Industrial Engineerig,1998,35(3):439-442.
[66]Adamopoulos G I,Pappis C P. A neighbourhood-based hybrid method for scheduling with fuzzy due-dates [J]. International Transactions in Operational Research,1998,5(3):147-153.
[67]Lam S S,Cai S. Single machine scheduling with nonlinear lateness cost functions and fuzzy due dates [J]. Nonlinear Analysis:Real World Applications,2002,3(2):307-316.
[68]吴 悦,汪定伟.用模拟退火法解任务的加工时间为模糊区间数的单机提前／拖期调度问题[J].信息与控制,1998,27(5)：394-400.
[69]王成尧,高 麟,汪定伟.模糊加工时间调度问题的研究[J].系统工程学报,1999,14(3)：238-242.
[70]唐国春,张 峰,罗守成.模糊加工时间排序问题的性质[J].运筹学报,1999,3(4)：84-94.
[71]王成尧,汪定伟.单机模糊加工时间下最迟开工时间调度问题[J].控制与决策,2000,15(1)：71-74.
[72]Lin FT. A job-shop scheduling problem with fuzzy processing times [A]. Proceedings of International Conference on Computational Science Part Ⅱ [C]. Berlin Heidelberg: Springer,2001.409-418.
[73]胡冬华.基于CPM和TOC的单件小批生产计划与调度方法研究.硕士学位论文,南京航空航天大学,2009
[74]郑晓伟.基于遗传算法的作业车间动态调度研究.硕士学位论文,武汉理工大学,2008
[75]聂书志.基于DNA遗传算法的协同制造资源优化配置技术研究.博士学位论文,华南理工大学,2010
[76]Sakawa M,Mori T. An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate [J]. Computers&Industrial Engineering,1999,36(2):325-341.
[77]ItohT,Ishi H. Fuzzy due-date scheduling problem with fuzzy processing time [J]. International Transactions in Operational Research,1999,6(6):639-647.
[78]Sakawa M,Kubota R. Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms [J]. European Journal of Operational Research,2000,120(2):393-407.
[79]Chanas S,Kasperski A. Minimizingmaximum lateness in a single machine scheduling problem with fuzzy processing times and fuzzy due dates [J]. Engineering Applications of Artificial Intelligence,2001,14(3):377-386.
[80]Chanas S,Kasperski A. On two single machine scheduling problems with fuzzy processing times and fuzzy due dates [J]. European Journal of Operational Research,2003,147(2):281-296.
[81]耿兆强,邹益仁.用遗传算法解决一类模糊流水车间调度问题[J].系统工程与电子技术,2002,24(16)：5-7.
[82]耿兆强,邹益仁.基于遗传算法的作业车间模糊调度问题的研究[J].计算机集成制造系统,2002,8(8)：616～620.
[83]万国华.用模糊调度系统求解动态Job Shop问题.系统工程理论与实践,2001,(8)：97-101
[84]吴秀丽.柔性作业车间动态调度问题研究.系统仿真学报,2008,20(14)：3828-3832
[85]Jackson J R. Simulation research on job shop production. Naval Res Log Quart,1957, 4(3):287-295
[86]Lawler E L, Lenstra J K, Rinnooy Kan A H G. Recent developments in deterministic sequencing and scheduling:a survey. In:Dempster M A H et al. (Eds.), Deterministic and Stochastic Scheduling. Reidel, Dordrecht,1982,35-73
[87]潘全科,朱剑英.作业车间动态调度研究.南京航空航天大学学报,2005,37(2)：262-268
[88]王锐.应用多种群遗传算法求解动态车间调度问题.硕士学位论文,大连理工大学,2009
[89]E.L. Garey, D.S.Johnson and R.Sethi, "The complexity of flowshop and job-shop scheduling," Mathematics of Operations Research, vol.1, pp.117-129,1976.
[90]M. Montazeri and L.N. Van Wassenhove, "Analyses of scheduling rules for an FMS,' Int. J. Prod. Res., vol.28, pp.785-802,1990.
[91]H. Chen, J. Ihlow and C. Lehmann, "A Genetic Algorithm for Flexible Job-Shop Scheduling," in IEEE International Conference on Robotics and Automation, Detroit, 1999, pp.1120-1125.
[92]M. Mastrolilli and L.M. Gambardella, "Effective neighbour-hood functions for the flexible job shop problem," Journal of Scheduling, vol.3, no.1, pp.3-20,2000.
[93]J.W. Barnes and J.B. Chambers, "Flexible Job Shop Scheduling by Tabu Search, Graduate Program in Operations Research and Industrial Engineering," Tech. Rep. ORP 9609, University of Texas, Austin,1996.
[94]N.M. Najid, S. Dauzere-Pere and A. Zaidat, "A modified simulated annealing method for Flexible Job Shop Scheduling Problem," in Proceedings of the 2002 International Conference on Systems, Man and Cybernetics. Piscataway, NJ:IEEE Press,2002, pp. 6-12.
[95]P. Brandimarte, "Routing and scheduling in a flexible job shop by tabu search," Annals of Operations Research, vol.41, pp.157-183,1993.
[96]E. Balas, "Machine scheduling via disjunctive graphs:an implicit enumeration algorithm," Operations Research, vol.17, pp.941-957,1969.
[97]张超勇；董星；王晓娟；李新宇；刘琼.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.机械工程学报,2010,46(11)：156-164
[98]张超勇,饶运清,李培根,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.
[99]Zhang CY, Li PG, Rao YQ, Li SX (2005) A new hybrid GA/SA algorithm for the job shop scheduling problem. Lecture Notes in Computer Science 3448:246-259
[100]E. Balas and A. Vazacopoulos, "Guided local search with shifting bottleneck for job shop scheduling," Management Science, vol.44, no.2, pp.262-275,1998.
[101]吴秀丽,孙树栋,杨展等.多目标柔性Job Shop调度问题的技术现状和发展趋势[J].计算机应用研究,2007.3,24(3)：1-5,9.
[102]Garey E L, Johnson D S, Sethi R. The complexity of flowshop and job-shop scheduling [J]. Mathematics of Operations Research,1976,1:117-129.
[103]李新宇.工艺规划与车间调度集成问题的求解方法研究.博士学位论文,华中科技大学,2009
[104]郑金华.多目标进化算法及其应用.北京：科学出版社.2007
[105]刘兰霞.多目标粒子群优化算法研究.硕士学位论文,湖南科技大学,2010
[106]张国辉.柔性作业车间调度方法研究.博士学位论文,华中科技大学,2009
[107]刘捷.不确定条件下基于遗传算法的柔性作业车间调度问题研究.硕士学位论 文,华中科技大学,2009
[108]D.E.Goldberg,Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA:Addison-Wesley,1989
[109]栾威.基于多目标遗传算法求解Steiner树问题.硕士学位论文,东北大学,2008
[110]张武华.SB公司多工厂生产协调优化问题研究.硕士学位论文,华南理工大学,2010
[111]Horn J, Nafpliotis N, Goldberg D E. A niched pareto genetic algorithm for multiobjective optimization. Proceedings of the first IEEE Conference on Evolutionary Computation, Piscataway USA,1994,82-87
[112]Srinivas N, Deb K. Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation,1994,2(3):221-248
[113]Zitzler E, Thiele L. Multiobjective evolutionary algorithms:a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation,1999,3(4):257-271
[114]Zitzler E, Laumanns M, Thiele L. SPEA2:improving the strength pareto evolutionary algorithm, TIK-Report 103,2001
[115]Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm NSGA-II. IEEE Transaction on Evolutionary Computation,2002, 6(2):182-197
[116]Zeng Sanyou, Kang Lishan, Ding Lixin. An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints Evolutionary Computation. MIT Press,2004
[117]郑金华,史忠植,谢勇.基于聚类的快速多目标遗传算法.计算机研究与发展,2004,41(7)：1081-1087
[118]郭秀萍.多目标进化算法及其在制造系统中的应用研究.博士学位论文,上海交通大学,2007
[119]张超勇,刘琼,邱浩波,邵新宇.考虑加工成本和时间的柔性作业车间调度问题研究.机械科学与技术,2009：28(8)：1005-1011
[120]Xia W J, Wu Z M. An effective hybrid optimization approach for multi objective flexible job shop scheduling problems. Computers & Industrial Engineering,2005, 48(2):409-425
[121]余建军,孙树栋,郝京辉.免疫算法求解多目标柔性作业车间调度研究.计算机集成制造系统,2006,12(10)：1643-1650
[122]Mahfoud SW (1995) Niching methods for genetic algorithms. Dissertation, University of Illinois at Urbana-Champaign
[123]Cui XX, Li M, Fang TJ (2001) Study of population diversity of multiobjective evolutionary algorithm based on immune and entropy principles. Proceedings of the 2001 Congress on Evolutionary Computation.Seoul:OPAC. pp 1316-1321
[124]Shimooka T, Shimizu K (2004) Artificial Immune System for Personal Identification with Finger Vein Pattern. Lecture Notes in Computer Science 3214: 511-518
[125]Xiao RB, Cao PB, Liu Y (2007) Engineering Immune Computing. Science Press, Beijing, China. In Chinese
[126]彭琰.基于生物激励机制的算法研究.硕士学位论文,湘潭大学,2007
[127]赵君莉,杨善学,王宇平.改进的非支配排序遗传算法INSGA-II西安科技大学学报,2006,26(4)：34-35
[128]Kacem I, Hammadi S, Borne P (2002) Pareto-optimality approach for flexible job-shop scheduling problems:hybridization of evolutionary algorithms and fuzzy logic. Math Comput Simul 60(3-5):245-276[130] Brandimarte P (1993) Routing and scheduling in a flexible job shop by taboo search. Ann Oper Res 41:157-183
[129]Xia WJ, Wu ZM (2005) An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Comput Ind Eng 48 (2): 409-425
[130]Zhang GH, Shao XY, Li PL, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng 56(4):1309-1318
[131]Xing LN, Chen YW, Yang KW (2009) An efficient search method for multi-objective flexible job shop scheduling problems. J Intell Manuf 20:283-293
[132]Thomas L. Saaty. An Exposition on the AHP in Reply to the Paper "Remarks on the Analytic Hierarchy Process".Management sience,1990,36(3):259-268
[133]Omar A.ghrayeb, A bi-criteria optimization:Minimizing the integral value and spread of the fuzzy makespan of job shop scheduling problems [J] Applied soft computing 2/3F (2003)197～210
[134]Tadahiko Murata, Hisao Ishibuchi, Mtsuo Gen, Multi-Objective Fuzzy Scheduling with the OWA Operator for Handling Different Scheduling Criteria and Different Job Importance [C], IEEE International Fuzzy systems conference Proceedings, August 22-25,1999, Seoul, Korea
[135]Masatoshi sakawa, Ryo Kubota. Fuzzy programming for multi-objective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithm. European Journal of Operational Research,2000,120(2):393～407
[136]Imed Kacem, Slim Hammadi and Pierre Borne. Pareto-optimality approach for flexible job-shop scheduling problems:hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation,2002,60(3-5):245-276
[137]Dobrila Petrovic, Alejandra Duenas and Sanja Petrovic. Decision Support Systems,2007,43(4):1527-1538
[138]Fu-ming Li, Yun-long Zhu, Chao-wan Yin et al. Fuzzy Programming for Multiobjective Fuzzy JobShop Scheduling with Alternative MachinesThrough Genetic Algorithms. L. Wang, K. Chen, and Y.S. Ong (Eds.):ICNC 2005, LNCS 3611, pp.992-1004,2005
[139]Deming Lei. Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems. International Journal of Advanced Manufacturing and Technology,2008,37:157-165
[140]阚震.基于遗传算法的作业车间多目标模糊调度问题研究.硕士学位论文,合肥工业大学,2007
[141]崔瑞娟.模糊项目调度优化算法研究.硕士学位论文,哈尔滨理工大学,2007
[142]程蓉.复杂生产环境下优化调度方法研究与系统实现.博士学位论文,华中科技大学,2006
[143]孙中华.基于颜色与目标轮廓特征的视频分割方法.硕士学位论文,吉林大学,2004
[144]张松顺.半监督学习方法及其应用研究.硕士学位论文,江南大学,2009
[145]宋璐.流程工业的不确定性生产调度研究.硕士学位论文,浙江工业大学,2006
[146]高原.基于浏览行为的用户兴趣量度.硕士学位论文,重庆大学,2006
[147]鲁燕飞.基于Rough集理论的风险决策评价方法研究和应用.硕士学位论文,南昌大学,2006
[148]雷德明,吴智铭.多目标模糊作业车间调度问题研究.计算机集成制造系统,2006,12(2)：174-179
[149]Masatoshi sakawa, Ryo Kubota. Fuzzy programming for multi-objective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithm. European Journal of Operational Research,2000,120(2):393-407
[150]J ackson J R. Simulation Research on Job Shop Production[J]. Naval Res. Log. Quart.,1957,4 (3):287-295.
[151]潘全科,朱剑英.作业车间动态调度研究[J].南京航空航天大学学报,2005,37(2)：262-268.
[152]Nelson R T, Holloway C A, Wong R M. Centralized Scheduling and Priority Implementation Heuristics for a Dynamic Job Shop Model with due Dates and Variable Processing Time [J]. AIIE Transactions,1977,19:96-102.
[153]Coello C A C. Evolutionary Multi-objective Optimization:a Historical View of the Field[J]. Computational Intelligence Magazine,2006,1 (1):28-36.
[154]Abumaizar R J, Svestka J A. Rescheduling Job Shops under Random Disruptions [J]. Int. J. Prod.Res.,1997,35:2065-2082.
[155]Raheja A S, Subramaniam V. Reactive Recovery of Job Shop Schedules-a Review [J]. Int. J. Adv.Manuf. Technol.,2002,19:756-763.
[156]连坤雷,张超勇,高亮,张朝阳.一种基于改进遗传算法的多目标动态调度优化.中国制造业信息化,2010,2：13-17
[157]Wong T N,Leung C W,Mak K L,et al. Integrated Process Planning and Scheduling/Rescheduling-an Agent-based Approach[J]. International Journal of Production Research,2006,44 (18/19):3627-3655.
[158]张超勇,李新宇,王晓娟,刘琼,高亮.基于滚动窗口的多目标动态调度优化研究,中国机械工程,2009,20(18)：2190-2197
[159]Raman N, Talbot F. The Job Shop Tardiness Problem:a Decomposition Approach [J]. European Journal of Operational Research,1993,69:187-199.
[160]Sabuncuoglu I,Karabuk S. Rescheduling Frequency in an FMS with Uncertainy Processing Times and Unreliable Machines [J]. Journal of Manufacturing Systems,1999,18 (4):268-281.