作业车间预反应式动态调度理论与方法研究
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摘要
随着全球市场竞争的加剧、客户需求的多样化与个性化，实际制造环境中紧急件、工件随机到达等状态频发，使得动态调度问题成为制造系统研究领域的热点之一。同时，随着经济的快速发展，环境问题日益被关注。因此，本文围绕动态环境下作业车间、柔性作业车间调度问题以及面向低碳运行的多目标柔性作业车间调度问题展开研究。
实际的制造车间中，往往存在很多动态不确定因素，如何保持车间生产稳定以及车间生产效率，制定合理的调度计划是非常重要的。本文结合传统作业车间调度问题的整数规划模型、动态调度问题特性与动态事件属性，建立了作业车间动态调度的数学模型。为了提高预-反应调度的鲁棒性，设计了一种带空闲时间的预-反应调度策略。实验结果表明，对于小规模生产车间，带空闲时间的预-反应调度策略可保证原始调度的鲁棒性且增强重调度的效率和稳定性；但对于大规模生产车间，完全重调度策略具有较好的调度性能。
实际制造系统中，有效的重调度求解方法对决策者进行决策具有重要影响。在充分考虑车间生产信息的基础上，本文结合遗传算法的全局搜索能力以及禁忌搜索算法的局部搜索能力，提出一种具有新初始化方法的遗传禁忌搜索算法求解单目标作业车间动态调度问题。实验结果表明，新初始化方法能保持种群多样性和提高算法的全局搜索能力，且提出的遗传禁忌搜索算法具有良好的鲁棒性。
在实际车间环境中，如何权衡调度效率和调度稳定性两方面的问题是解决动态调度问题的关键。本文建立了基于调度效率和调度稳定性的多目标作业车间动态调度模型，设计了基于遗传禁忌搜索算法的动态调度求解方法，在该方法中，任何一个调度周期内，发生器为下一阶段产生动态事件，遗传禁忌搜索算法优化问题并产生预调度方案。实例验证了提出的模型和求解方法的有效性与优越性。
针对柔性作业车间动态调度系统，本文在传统指标中添加了平均工序数量参数，完善了柔性作业车间动态调度的评价指标体系，建立了柔性作业车间动态调度模型，设计了一种有效的遗传变邻域算法，实例验证了该方法的有效性。采用实验手段探讨了调度周期对调度系统的影响，结果表明不同车间负荷水平下的调度周期与调度系统性能均大致呈U型曲线。采用ANOVA方法进行统计显著性试验，结果表明车间负荷水平、新工件数量均对调度效率和调度稳定性具有统计显著影响。
随着全球气候变暖与经济快速增长，降低能耗成为国际政治、经济与学术研究关注的热点之一。首先，利用机床加工工序过程的总空载能耗以及能量利用率等公式，设计了用于动态调度的基于工序加工时空载能耗的评价指标。接着，采用两种方式研究了面向低碳运行的多目标柔性作业车间动态调度问题。一是，建立了面向低碳运行的能耗与调度效率的目标规划模型，设计了带精英策略的遗传算法的求解方法，实验结果表明，最小能耗模型可有效降低能耗，且在一定程度上提高调度效率。二是，建立了综合考虑能耗、调度效率与调度稳定性的多目标柔性作业车间动态调度模型，实验结果表明该模型可有效降低能耗，保证调度效率和调度稳定，这对应对气候变暖、要求低碳运行的大环境是有益的。最后，采用ANOVA方法进行统计显著性试验，结果表明车间负荷水平、新工件数量均对能耗、调度效率与调度稳定性具有统计显著影响。
在以上研究成果的基础上，根据某发动机冷却风扇加工车间的实际情况，分析了该车间存在的问题，将上述理论成果应用于实际车间生产进行了实例测试与分析。
最后，对全文工作进行总结，并对今后研究方向进行展望。
With the increasing competition of global market and the diversity of customerdemand, many dynamic events have been occured in the real manufacturing. The dynamicscheduling problems have become one of the hot topics in the field of manufacturingsystem. Moreover, with the increasing rapid development of global economy,environmental problems are becoming the focus of attention. As a new sustainablemanufacturing mode, low carbon manufacturing is an effective way to realize the pledgefor conserving energy and reducing emissions in our country by2012. This paper focuseson the research of the dynamic job shop scheduling problem, the dynamic flexible jobshop scheduling problem and the multi-objective dynamic flexible job shop schedulingproblem based on low carbon.
Dynamic events always occur in the real shop floor. It is important to keep thescheduling stability, improve the scheduling efficiency and make reasonable schedulingdecisions. According with the integer programming for the classical job shop schedulingproblem and the characteristic of the dynamic events, a mathematical model for thedynamic job shop scheduling problem is proposed. In order to improve the robust of thepredictive/active scheduling strategy, a predictive/active scheduling strategy with insertingidle time is proposed. The experimental results show that the proposed strategy can keepthe robust of the original schedule and improve the scheduling efficiency and thescheduling stability in small scale shop floor. However, the complete rescheduling strategyhas a better scheduling performance in large scale shop floor.
An effective rescheduling approach has important impact on making decisions in realmanufacturing system. One hybrid algorithm which is mixed by the genetic algorithmwith strong global searching ability and tabu search with strong local searching ability hasbeen proposed to solving the dynamic job shop scheduling problem. The experimentalresults show that the new initialization can keep the population diversity and improve theglobal search ability. They also show that the hybrid genetic algorithm and tabu searchalgorithm has the good robustness.
How to balance the scheduling efficiency and the scheduling stability is a keyproblem to solving the dynamic job shop problem in real shop floor. In this paper, amulti-objective mathematical model for dynamic job shop scheduling problem, whichcontains the scheduling efficiency and the scheduling stability, has been proposed. Ahybrid genetic algorithm and tabu search algorithm is proposed to solve themulti-objective dynamic job shop scheduling problem. The simulator generates the dynamic events for next phase at each rescheduling point. The hybrid algorithm optimizesthe problem and generates the prediction schedules. The experimental results show theeffectiveness and advantage of the proposed model and the proposed approach.
Dynamic flexible job shop scheduling problem is one of the extension of dynamic jobshop scheduling problem. This paper inserts a mean quantity of operations to improve theevaluation system of dynamic flexible job shop scheduling problem. The mathematicalmodel for dynamic flexible job shop scheduling problem has been proposed. An effectivealgorithm, which mixes the genetic algorithm and variable neighborhood search algorithm,has been proposed to solve the multi-objective dynamic flexible job shop schedulingproblem. The experimental results show the feasible of the proposed approach. Theexperimental results also reveal that the curve of the shop load level and the schedulingperformance is U-shape. The ANOVA method shows that the shop load level, new jobarrivals has a statistical influence on the scheduling efficiency and the scheduling stability.
With global warming and the increasing rapid development of global economy,reducing energy consumption has become one of the hot topics in the research ofinternational politics, economy and the academic research. This paper designs the energyconsumption according to the operation-based processing unload energy consumption. Agoal programming model based on low carbon has been proposed. An improved geneticalgorithm with elitist strategy has been proposed to solve dynamic flexible job shopscheduling problem. The experimental results show that the minimization energyconsumption model can reduce the energy consumption and enhance the schedulingefficiency partly. Moreover, a multi-objective dynamic flexible job shop scheduling model,which contains the energy consumption, the scheduling efficiency and the schedulingstability, has been proposed. The experimental results show that the proposed model canreduce the energy consumption, improve the scheduling efficiency and keep thescheduling stability. Finally, the ANOVA method shows that the shop load level, new jobarrivals has a statistical influence on the scheduling efficiency and the scheduling stability.
Based on the research word mentioned above and the real conditions in the enginecooling fan shop floor, the issues in the shop floor have been analyzed. The above researchresults have been applied into the shop floor to test and analyze.
Finally, the research results achieved in the dissertation is summarized and futurework is generalized and looked forward.
实际的制造车间中，往往存在很多动态不确定因素，如何保持车间生产稳定以及车间生产效率，制定合理的调度计划是非常重要的。本文结合传统作业车间调度问题的整数规划模型、动态调度问题特性与动态事件属性，建立了作业车间动态调度的数学模型。为了提高预-反应调度的鲁棒性，设计了一种带空闲时间的预-反应调度策略。实验结果表明，对于小规模生产车间，带空闲时间的预-反应调度策略可保证原始调度的鲁棒性且增强重调度的效率和稳定性；但对于大规模生产车间，完全重调度策略具有较好的调度性能。
实际制造系统中，有效的重调度求解方法对决策者进行决策具有重要影响。在充分考虑车间生产信息的基础上，本文结合遗传算法的全局搜索能力以及禁忌搜索算法的局部搜索能力，提出一种具有新初始化方法的遗传禁忌搜索算法求解单目标作业车间动态调度问题。实验结果表明，新初始化方法能保持种群多样性和提高算法的全局搜索能力，且提出的遗传禁忌搜索算法具有良好的鲁棒性。
在实际车间环境中，如何权衡调度效率和调度稳定性两方面的问题是解决动态调度问题的关键。本文建立了基于调度效率和调度稳定性的多目标作业车间动态调度模型，设计了基于遗传禁忌搜索算法的动态调度求解方法，在该方法中，任何一个调度周期内，发生器为下一阶段产生动态事件，遗传禁忌搜索算法优化问题并产生预调度方案。实例验证了提出的模型和求解方法的有效性与优越性。
针对柔性作业车间动态调度系统，本文在传统指标中添加了平均工序数量参数，完善了柔性作业车间动态调度的评价指标体系，建立了柔性作业车间动态调度模型，设计了一种有效的遗传变邻域算法，实例验证了该方法的有效性。采用实验手段探讨了调度周期对调度系统的影响，结果表明不同车间负荷水平下的调度周期与调度系统性能均大致呈U型曲线。采用ANOVA方法进行统计显著性试验，结果表明车间负荷水平、新工件数量均对调度效率和调度稳定性具有统计显著影响。
随着全球气候变暖与经济快速增长，降低能耗成为国际政治、经济与学术研究关注的热点之一。首先，利用机床加工工序过程的总空载能耗以及能量利用率等公式，设计了用于动态调度的基于工序加工时空载能耗的评价指标。接着，采用两种方式研究了面向低碳运行的多目标柔性作业车间动态调度问题。一是，建立了面向低碳运行的能耗与调度效率的目标规划模型，设计了带精英策略的遗传算法的求解方法，实验结果表明，最小能耗模型可有效降低能耗，且在一定程度上提高调度效率。二是，建立了综合考虑能耗、调度效率与调度稳定性的多目标柔性作业车间动态调度模型，实验结果表明该模型可有效降低能耗，保证调度效率和调度稳定，这对应对气候变暖、要求低碳运行的大环境是有益的。最后，采用ANOVA方法进行统计显著性试验，结果表明车间负荷水平、新工件数量均对能耗、调度效率与调度稳定性具有统计显著影响。
在以上研究成果的基础上，根据某发动机冷却风扇加工车间的实际情况，分析了该车间存在的问题，将上述理论成果应用于实际车间生产进行了实例测试与分析。
最后，对全文工作进行总结，并对今后研究方向进行展望。
With the increasing competition of global market and the diversity of customerdemand, many dynamic events have been occured in the real manufacturing. The dynamicscheduling problems have become one of the hot topics in the field of manufacturingsystem. Moreover, with the increasing rapid development of global economy,environmental problems are becoming the focus of attention. As a new sustainablemanufacturing mode, low carbon manufacturing is an effective way to realize the pledgefor conserving energy and reducing emissions in our country by2012. This paper focuseson the research of the dynamic job shop scheduling problem, the dynamic flexible jobshop scheduling problem and the multi-objective dynamic flexible job shop schedulingproblem based on low carbon.
Dynamic events always occur in the real shop floor. It is important to keep thescheduling stability, improve the scheduling efficiency and make reasonable schedulingdecisions. According with the integer programming for the classical job shop schedulingproblem and the characteristic of the dynamic events, a mathematical model for thedynamic job shop scheduling problem is proposed. In order to improve the robust of thepredictive/active scheduling strategy, a predictive/active scheduling strategy with insertingidle time is proposed. The experimental results show that the proposed strategy can keepthe robust of the original schedule and improve the scheduling efficiency and thescheduling stability in small scale shop floor. However, the complete rescheduling strategyhas a better scheduling performance in large scale shop floor.
An effective rescheduling approach has important impact on making decisions in realmanufacturing system. One hybrid algorithm which is mixed by the genetic algorithmwith strong global searching ability and tabu search with strong local searching ability hasbeen proposed to solving the dynamic job shop scheduling problem. The experimentalresults show that the new initialization can keep the population diversity and improve theglobal search ability. They also show that the hybrid genetic algorithm and tabu searchalgorithm has the good robustness.
How to balance the scheduling efficiency and the scheduling stability is a keyproblem to solving the dynamic job shop problem in real shop floor. In this paper, amulti-objective mathematical model for dynamic job shop scheduling problem, whichcontains the scheduling efficiency and the scheduling stability, has been proposed. Ahybrid genetic algorithm and tabu search algorithm is proposed to solve themulti-objective dynamic job shop scheduling problem. The simulator generates the dynamic events for next phase at each rescheduling point. The hybrid algorithm optimizesthe problem and generates the prediction schedules. The experimental results show theeffectiveness and advantage of the proposed model and the proposed approach.
Dynamic flexible job shop scheduling problem is one of the extension of dynamic jobshop scheduling problem. This paper inserts a mean quantity of operations to improve theevaluation system of dynamic flexible job shop scheduling problem. The mathematicalmodel for dynamic flexible job shop scheduling problem has been proposed. An effectivealgorithm, which mixes the genetic algorithm and variable neighborhood search algorithm,has been proposed to solve the multi-objective dynamic flexible job shop schedulingproblem. The experimental results show the feasible of the proposed approach. Theexperimental results also reveal that the curve of the shop load level and the schedulingperformance is U-shape. The ANOVA method shows that the shop load level, new jobarrivals has a statistical influence on the scheduling efficiency and the scheduling stability.
With global warming and the increasing rapid development of global economy,reducing energy consumption has become one of the hot topics in the research ofinternational politics, economy and the academic research. This paper designs the energyconsumption according to the operation-based processing unload energy consumption. Agoal programming model based on low carbon has been proposed. An improved geneticalgorithm with elitist strategy has been proposed to solve dynamic flexible job shopscheduling problem. The experimental results show that the minimization energyconsumption model can reduce the energy consumption and enhance the schedulingefficiency partly. Moreover, a multi-objective dynamic flexible job shop scheduling model,which contains the energy consumption, the scheduling efficiency and the schedulingstability, has been proposed. The experimental results show that the proposed model canreduce the energy consumption, improve the scheduling efficiency and keep thescheduling stability. Finally, the ANOVA method shows that the shop load level, new jobarrivals has a statistical influence on the scheduling efficiency and the scheduling stability.
Based on the research word mentioned above and the real conditions in the enginecooling fan shop floor, the issues in the shop floor have been analyzed. The above researchresults have been applied into the shop floor to test and analyze.
Finally, the research results achieved in the dissertation is summarized and futurework is generalized and looked forward.
引文
[1] Garey E L, Johnson D S, Sethi R. The Complexity of Flowshop and Job-ShopScheduling. Mathematics of Operations Research,1976,1:117-129.
[2]聂黎.基于GEP的车间动态调度方法与技术研究:[博士学位论文].武汉:华中科技大学,2011.
[3]齐二石，孔海宁，何曙光.钢铁企业副产煤气多周期动态优化调度.系统工程理论与实践.2010(11):2071-2079.
[4]熊禾根.模具企业动态车间作业计划研究:[博士学位论文].武汉:华中科技大学,2005.
[5] Jackson J. R., Simulation Research on Job Shop Production. Naval Research LogisticsQuarterly,1957.4(3):287-295.
[6] Ramash R. Dynamic Job Shop Scheduling--A Review of Simulation Research.OMEGA,1990,18(1):43-57.
[7] Szelde Elizabeth Kerr, Roger M. Knowledge-Based Reactive Scheduling. ProductionPlanning&Control,1994.5(5):124-145.
[8] Suresh V., Chandhuri D. Dynamic Scheduling--A Survey of Research. InternationalJournal of Production Economics,1993.32(1):53-63.
[9] Vieira G. E., Hermann, J. W., Lin, E. Rescheduling Manufacturing Systems: AFramework of Strategies, Policies and Methods. Journal of Scheduling.2003.6(1),36-92.
[10] Aytug H, Lawley MA, Mckay K, et al. Executing Production Schedules in the Face ofUncertainties: A Review and Some Future Directions.5th International Conferenceon Industrial Engineering and Production Management (IEPM01), Aug20-24,2001,Quebec City, Canada. European Journal of Operational Research,2005,161(1):86-110.
[11] Potts CN, Strusevich VA. Fifty Years of Scheduling: A Survey of Milestones. Journalof the Operational Research Society,2009,60, S41--S68.
[12] Ouelhadj D., Petrovic S., A Survey of Dynamic Scheduling in ManufacturingSystems. Journal of Scheduling,2009.12,417-431.
[13] Mehta S. V., Uzsoy R. Predictable Scheduling of a Single Machine Subject toBreakdowns. International Journal of Computer Integrated Manufacturing,1999.12(1),15-38.
[14] Vieira G.E., Herrmann J.W., Lin E. Analytical Models to Predict the Performance of aSingle Machine System under Periodic and Event-Driven Rescheduling Strategies.International Journal of Production Research,2000.38(8),1899-1915.
[15] Renna P., Job Shop Scheduling by Pheromone Approach in a Dynamic Environment.International Journal of Computer Integrated Manufacturing,2010.23(5),412-424.
[16]钱晓龙,唐立新,刘文新.动态调度的研究方法综述,.控制与决策.2001,16(2):141-145.
[17] Leon V.J. Wu S.D. Storer R.H. Robustness Measures and Robust Scheduling for JobShops. IIE Transactions,1994,26(5),32-41.
[18] Cowling P. I. Johansson M. Using Real-Time Information for Effective DynamicScheduling. European Journal of Operational Research.2002,139(2):230-244.
[19] Ruedee Rangsaritratsamee, William G. Ferrell Jr., Mary Beth Kurz. DynamicRescheduling that Simultaneously Considers Efficiency and Stability. Computers&Industrial Engineering,2004,46:1-15.
[20] Abumaizar, R.J., J.A. Svestka. Rescheduling Job Shops under Random Disruptions.International Journal of Production Research,1997.35(7):2065-2082.
[21]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.
[22]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,(18):2190-2197.
[23] Jian A K, Elmaraghy H A. Production Scheduling Rescheduling in FlexibleManufacturing. International Journal of Production Research,1997,35(1):281-309.
[24] Tay J C, Ho N B. Evolving Dispatching Rules Using Genetic AlgorithmProgramming for Solving Multi-Objective Flexible Job Shop Problem. Computer&Industrial engineering,2008,54(3):453-473.
[25] Lawrence SR, Sewell EC. Heuristic, Optimal, Static and Dynamic Schedules whenProcessing Times are Uncertain. Journal of Operations Management,1997,15(1):71-82.
[26] Sanjay V. Mehta., Reha M. Uzsoy. Predictable Scheduling of a Job Shop Subject toBreakdowns. IEEE Transactions on robotics and automation,1998,14(3):365-378.
[27] O'Donovan, R., R. Uzsoy., K.N. McKay. Predictable Scheduling of a Single Machinewith Breakdowns and Sensitive Jobs. International Journal of Production Research.1999,37(18):4217-4233.
[28]李素粉,朱云龙,尹朝万.具有随机加工时间和机器故障的流水车间调度.计算机集成制造系统,2005,11(10):1245-1249.
[29]刘琳,谷寒雨,席裕庚.加工时间不确定的Just-in-Time单机鲁棒调度.2007,22(10):1151-11541159.
[30] Sabuncuoglu I., Bayiz M. Analysis of Reactive Scheduling Problems in a Job ShopEnvironment. European Journal of Operational Research.2000,126(3):567-586.
[31] Church LK, Uzsoy R. Analysis of Periodic and Event-Driven Rescheduling Policiesin Dynamic Shops. International Journal of Computer Integrated Manufacturing,1992,5(3):153-163.
[32]方剑，席裕庚.基于事件驱动的job shop仿真调度系统.系统仿真学报.1997.12:42-50.
[33]王冰，席裕庚，谷寒雨.一类单机动态调度问题的改进滚动时域方法.控制与决策.2005,20(3):257-260.
[34]刘越洋，席裕庚.基于两步滚动的单机调度算法研究.计算机工程.2004,30(24).144-146164.
[35] Sabuncuoglu I, Karabuk S. Rescheduling Frequency in an FMS with UncertainProcessing Times and Unreliable Machines. Journal of Manufacturing Systems,1999,18(4):268-281.
[36]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.
[37]吴秀丽.多目标柔性作业车间调度技术研究:[博士学位论文].西安：西北工业大学.2006.
[38]张国辉.柔性作业车间调度方法研究:[博士学位论文].武汉：华中科技大学,2009.
[39]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].武汉：华中科技大学,2009.
[40]鞠全勇，朱剑英.基于混合遗传算法的动态车间调度系统的研究.中国机械工程.2007,18(1):40-43.
[41]胡咏梅，贾磊，李歧强.一种基于动态粗集的工件滚动调度识别方法.机械工程学报.2005,41(3).67-71.
[42]刘明周，单晖，蒋增强，葛茂根,扈静,张铭鑫.不确定条件下车间动态重调度优化方法.机械工程学报.2009,45(10).137-142.
[43]王晓娟.多目标柔性作业车间调度方法研究:[博士学位论文].武汉：华中科技大学,2011.
[44] Leus, R., Herroelen, W. The complexity of Machine Scheduling for Stability with aSingle Disrupted Job. Operations Research Letters,2005.33(2),151-156.
[45] Wong T N,Leung C W, Mak K L, et al. Integrated Process Planning andScheduling/Rescheduling–an Agent-based Approach. International Journal ofProduction Research,2006,44(18-19):3627-3655.
[46] Ouelhadj, D., Cowling, P. I., Petrovic, S. Utility and Stability Measures forAgent-Based Dynamic Scheduling of Steel Continuous Casting. In Proceedings ofthe IEEE International Conference on Robotics and Automation. Taipei, Taiwan.2003.175-180.
[47] Daniels, R. L., Kouvelis, P. Robust Scheduling to Hedge Against Processing TimeUncertainty in Single-Stage Production. Management Science,1995.41(2),363–737.
[48] Jensen, M. T. Improving Robustness and Flexibility of Tardiness and TotalFlow-Time Job Shops Using Robustness Measures. Applied Soft Computing,2001,1(1),35–52.
[49] Fattahi P, Fallahi A. Dynamic Scheduling in Flexible Job Shop Systems byConsidering Simultaneously Efficiency and Stability. CIRP Journal of ManufacturingScience and Technology.2010,2(2):114-123.
[50] Sun, J., Xue, D. A Dynamic Reactive Scheduling Mechanism for Responding toChanges of Production Orders and Manufacturing Resources. Computers in Industry,2001,46(2),189–207.
[51] Dorn, J., Kerr, R. M., Thalhammer, G. Reactive Scheduling: Improving theRobustness of Schedules and Restricting the Effects of Shop Floor Disturbances byFuzzy Reasoning. International Journal of Human Computer Studies,1995,42,687–704.
[52]丁雷,王爱民,宁汝新.工时不确定条件下的车间作业调度技术.计算机集成制造系统,2010.16(1):98-108.
[53]黄振刚，鲁建厦，王成. MES环境下作业车间多级动态调度方法研究.机械设计与制造.2011(11):240-242.
[54] Liping Zhang, Liang Gao, Xinyu Li. A Hybrid Algorithm for Job Shop SchedulingProblems with Unexpected Disruptions, International Journal of AdvancedManufacturing Technology,2012, DOI:10.1007/s00170-012-4245-6.
[55] Goren S., Sabuncuoglu I. Robustness and Stability Measures for Scheduling:Single-Machine Environment. IIE Transactions,2008.40(1):66-83.
[56] Matsuura H., Tsubone H., Kanezashi M. Sequencing, Dispatching and Switching in aDynamic Manufacturing Environment. International Journal of Production Research,1993.31(7):1671-1688.
[57] Holloway C. A., Nelson R. T. Job Shop Scheduling with Due Dates and VariableProcessing Times. Management Science,1974.20,1264-1275.
[58] Pierreval H., Mebarki N., Dynamic Scheduling Selection of Dispatching Rules forManufacturing System. International Journal of Production Research,1997.35(6),1575-1591.
[59] Rajendran C., Holthaus O., A Comparative Study of Dispatching Rules in DynamicFlowshops and Jobshops. European Journal of Operational Research,1999.116,156-170.
[60] Subramaniam V., Lee G.K., Ramesh T., Hong G.S., Wong Y.S., Machine SelectionRules in A Dynamic Job Shop. Internal Journal of Advanced ManufacturingTechnology,2000.16,902-908.
[61]方剑,席裕庚,周期性和时间驱动的Job Shop滚动调度策略.控制与决策,1997.12(2):159-162166.
[62] Dominic, P.D.D., Kaliyamoorthy S., Kumar M.S., Efficient Dispatching Rules forDynamic Job Shop Scheduling. Internal Journal of Advanced ManufacturingTechnology,2004.24,70-75.
[63]易平，李建军，熊禾根.基于多级优先规则的模具企业动态车间作业调度算法,模具工业.2009,35(5):7-9.
[64] Alpay S, Yuzugullu N., Dynamic Job Shop Scheduling for Missed Due DatePerformance. International Journal of Production Research,2009.47(15),4047-4062.
[65] Nie L., Shao X.Y., Gao L., Li W., Evolving Scheduling Rules with Gene ExpressionProgramming for Dynamic Single-Machine Scheduling Problems. InternationalJournal of Advanced Manufacturing Technology,2010.50,729-747.
[66]聂黎，高亮，胡译丹.基于前序基因表达式编程的单机成组调度算法.计算机集成制造系统.2007,13(11):2261-2275.
[67]李鹏，刘民，吴澄.一类并行机调度问题的动态调度算法.计算机集成制造系统.2007(3):568-572.
[68]刘晓冰，李忠凯，黄学文,宋清杰.一种面向柔性生产的动态调度模型.计算机应用研究.2009(9):3239-3241.
[69] Branke J, Mattfeld D C. Anticipation and Flexibility in Dynamic Scheduling.International journal of production research.2005,43(15):3103-3129.
[70] Chryssolouris G., Subramaniam V. Dynamic Scheduling of Manufacturing Job ShopsUsing Genetic Algorithms. Journal of Intelligent Manufacturing,2001,12,281-293.
[71]刘爱军，杨育，邢青松，等.柔性作业车间多目标动态调度.计算机集成制造系统.2011(12):2629-2637.
[72]胡咏梅,基于粗集的车间动态调度研究:[博士学位论文],青岛:山东大学博士学位论文.2004.
[73] Liu S.Q., Ong H.L., Ng. K. M. Metaheuristics for Minimizing the Makespan of TheDynamic Shop Scheduling Problem. Advances in Engineering Software,2005,36,199-205.
[74] Liu S.Q. Ong H.L. Metaheuristics for the Mixed Shop Scheduling. Asia-PacificJournal of Operational Research (APJOR),2004.21(1):97-115.
[75] Malve S., Uzsoy R. A Genetic Algorithm for Minimizing Maximum Lateness onParallel Identical Batch Processing Machines with Dynamic Job Arrivals andIncompatible Job Families. Computers&Operations Research,2007.34,3016-3028.
[76] Zhou R., Nee A. Y. C., Lee H. P. Performance of an Ant Colony OptimizationAlgorithm in Dynamic Job Shop Scheduling Problems. International Journal ofProduction Research,2009,47(11):2903-2920.
[77] Zhou, R., H.P. Lee, A.Y.C. Nee, Applying Ant Colony Optimisation (ACO) Algorithmto Dynamic Job Shop Scheduling Problems. International Journal of ManufacturingResearch.20083(3):301-320.
[78] Adibi, M.A., Zandieh M., Amiri M., Multi-Objective Scheduling of Dynamic JobShop Using Variable Neighborhood Search. Expert Systems with Applications,2010.37(1),282-287.
[79] Zandieh M., Adibi M.A., Dynamic Job Shop Scheduling Using VariableNeighbourhood Search. International Journal of Production Research,2010.48(8),2449-2459.
[80]王策,王书锋,冯冬青,梁燕.混合粒子群算法在Job-Shop动态调度中的应用.计算机工程与应用.201046(26):219-222.
[81]魏英姿，谷侃锋.基于性能预测的遗传强化学习动态调度方法.系统仿真学报.2010(12):2809-2812.
[82]杨红红,吴智铭.基于自适应遗传算法的柔性动态调度研究.中国机械工程,2002,13(21):1845-1848.
[83]余建军,孙树栋,王军强等.免疫模拟退火算法及其在柔性动态Job Shop中的应用.中国机械工程,2007,18(7):793-799.
[84] Dewan P., Joshi S. Auction-Based Distributed Scheduling in a Dynamic Job ShopEnvironment. International Journal of Production Research,2002.40(5):1173-1191.
[85]张晴,饶运清,车间动态调度方法研究.机械制造,2003.41(461):39-41.
[86] Xiang, W. H.P. Lee, Ant Colony Intelligence in Multi-Agent Dynamic ManufacturingScheduling. Engineering Applications of Artificial Intelligence,2008.21(1):73-85
[87]徐新黎,郝平,王万良.多Agent动态调度方法在染色车间调度中的应用.计算机集成制造系统.2010,16(3):611-620.
[88] Rajabinasab A, Mansour S. Dynamic Flexible Job Shop Scheduling with AlternativeProcess Plans: an Agent-Based Approach. International Journal of AdvancedManufacturing Technology.2011,54(9-12):1091-1107.
[89] Nejad, H.T.N., N. Sugimura. Iwamura, Agent-Based Dynamic Integrated ProcessPlanning and Scheduling in Flexible Manufacturing Systems. International Journal ofProduction Research,2011.49(5):1373-1389.
[90] Kouiss Khalid, Pierreval Henri, Nasser Mebarki. Using Multi-Agent Architecture inFMS for Dynamic Scheduling. Journal of Intelligent Manufacturing,1997,8(1):41-47.
[91]宋娟.多Agent分布式车间动态调度仿真系统研究.制造业自动化.2011(24):57-58.
[92]王国磊，林琳，钟诗胜.基于模糊聚类的Q-学习在动态调度中的应用.计算机集成制造系统.2009(4):751-757.
[93] Ramasesh R. Dynamic Job Shop Scheduling: A Survey of Simulation Research.Omega,1990.18(1):43-57.
[94] P Priore, D De La Fuente, A Gomez, J Puente. A Review of Machine Learning inDynamic Scheduling of Flexible Manufacturing Systems. AI EDAM,2001.15(3):251-263.
[95] Priore, P., Furnte D., Puente J., Parreno J. A Comparison of Machine-LearningAlgorithms for Dynamic Scheduling of Flexible Manufacturing Systems.Engineering Applications of Artificial Intelligence,2006.19(3):247-255.
[96] IMC26. Energy Efficient&Low Carbon Manufacturing, Sept. Dublinhttp://internationalmanufacturingconference.com/Location.php,2009.
[97] S A Melnyk, R P Sroufe, F L Montabon, T J Hinds. Green MRP: Identifying theMaterial and Environmental Impacts of Production Schedules. International Journalof Production Research,2001,39(8):1559-1573.
[98] STC C2009Annual report. The Scientific Technical Committee Cutting-CIRP,2009
[99]路甬祥.走向绿色和智能制造——中国制造发展之路.中国机械工程.2010(4):379-386.
[100]唐德才，宋平，李长顺.中国制造业低碳发展现状研究.产业与科技论坛.2011(21):5-7.
[101]刘清涛，蔡宗琰，刘晓婷，等.一种面向绿色制造的多目标车间调度方法.长安大学学报(自然科学版).2011(2):106-110.
[102]唐任仲，贾顺，吕景祥.基于活动的产品车间制造过程碳排量计算方法.机电工程.2011(6):639-644.
[103]刘献礼，陈涛.机械制造中的低碳制造理论与技术.哈尔滨理工大学学报.2011(1):1-8.
[104]何彦.面向绿色制造的机械加工系统任务优化调度方法研究:[博士学位论文].重庆：重庆大学,2007.
[105]刘飞，李聪波，曹华军，等.基于产品生命周期主线的绿色制造技术内涵及技术体系框架.机械工程学报.2009(12):115-120.
[106]李聪波，刘飞，谭显春，等.基于风险矩阵和模糊集的绿色制造实施风险评估方法.计算机集成制造系统.2010(1):209-214.
[107]刘飞，曹华军.绿色制造的理论体系框架.中国机械工程.2000(9):10-13.
[108]谭显春，刘飞，曹华军，等.绿色制造的一种工艺路线决策模型及其求解算法.机械工程学报.2004(4):154-159.
[109]谭显春，刘飞，曹华军，等.面向绿色制造的刀具选择决策模型及其应用.重庆大学学报(自然科学版).2003(3):117-121.
[110]何彦，刘飞，曹华军，等.面向绿色制造的工艺规划支持系统及应用.计算机集成制造系统.2005(7):975-980.
[111]何彦，刘飞，曹华军，等.面向绿色制造的机械加工系统任务优化调度模型.机械工程学报.2007(4):27-33.
[112]王永靖，刘飞，王琦峰.面向绿色制造的汽车制造企业信息系统及关键技术研究.中国机械工程.2009(7):832-837.
[113]曹华军，刘飞，何彦.机械加工系统节能降噪型综合任务分配模型及其应用.机械工程学报.2006(5):97-102.
[114] He Y, Liu F, Cao H J, et al. A Bi-Objective Model for Job-Shop Scheduling Problemto Minimize Both Energy Consumption and Makespan. Journal of Central SouthUniversity of Technology.2005,122:167-171.
[115] He Y, Liu F. Methods for Integrating Energy Consumption and EnvironmentalImpact Considerations into the Production Operation of Machining Processes.Chinese Journal of Mechanical Engineering.2010,23(4):428-435.
[116] Industrial Assessment Centers. http://www1.eere.energy.gov/industry/best practices/iacs. html,2009.
[117] Gong Yunqi, Lv Min, Wang Gang, et.al, Calculation and Prediction Of EnergyConsumption in Discrete Manufactures Based on Knowledge,14th InternationalConference on Industrial Engineering and Engineering Management, Tianjin, China,OCT20-22,2007.
[118] Gutowski T, Murphy C, Allen D, etc. Environmentally Benign Manufacturing:Observations from Japan, Europe and the United States. Journal of CleanerProduction,2005,13:1-17.
[119] Dietmair A, Verl A. A Generic Energy Consumption Model for Decision Making andEnergy Efficiency Optimization in Manufacturing. International Journal ofSustainable Engineering,2009,2(2):123-133.
[120] Dietmair A, Verl A. Energy Consumption Forecasting and Optimisation for ToolMachines. Modern Machinery Science Journal,2009(3):62-67
[121] Bauer D J, Thurwachter S, Sheng P S. Integration of Environmental Factors inSurface Planning: Part1--Mass and Energy Modeling, Transactions of NAMRI/SME,1998,26:171-176
[122] Cai Y P, Huang G H, Lin Q G, et al. An Optimization-Model-Based InteractiveDecision Support System for Regional Energy Management Systems Planning UnderUncertainty. Expert Systems with Applications,2009,36:3470-3482.
[123] Rockwell Automation. Extending the Scope of MES to Achieve Energy Efficiencyand Sustainability Goals. Publication SUST-WP001A-EN-P, May2009.
[124] Herrmann C, Thiede S. Process Chain Simulation to Foster Energy Efficiency inManufacturing. CIRP Journal of Manufacturing Science and Technology,2009,1:221-229
[125]张超勇.基于自然算法的作业车间调度问题理论与应用研究:[博士学位论文].武汉:华中科技大学,2006.
[126] Van Hulle MM. A Goal Programming Network for Mixed Integer LinearProgramming: A Case Study for the Job-Shop Scheduling Problem. InternationalJournal of Neural Systems,1991,2(3):201-209
[127]刘琳.动态不确定环境下生产调度算法研究:[博士学位论文].上海:上海交通大学,2007.
[128] Bierwirth C. Mattfeld D.C. Production Scheduling and Rescheduling with GeneticAlgorithms. Evolutionary Computation,1999,7(1):1-17
[129] Church L.K. and Uzsoy R. Analysis of Periodic and Event-Driven ReschedulingPolicies in Dynamic Shops. International Journal of Computer IntegratedManufacturing,1992,5(3):153-163
[130] Turkcan A., Akturk M. S., Storer R.H. Predictive/Reactive Scheduling withControllable Processing Times and Earliness-Tardiness Penalties. IIE Transactions,2009.41(12),1080-1095.
[131] Vinod V., Sridharan R. Simulation Modeling and Analysis of Due-Date AssignmentMethods and Scheduling Decision Rules in A Dynamic Job Shop Production System.International Journal of Production Economics,2011.129(1):127-146.
[132] Park BJ, Choi HR, Kim HS. A Hybrid Genetic Algorithm for the Job ShopScheduling Problems. Computers&Industrial Engineering,2003,45(4):597–613.
[133] Zhang, C.Y., Rao, Y.Q. Li, P.G. An Effective Hybrid Genetic Algorithm for the JobShop Scheduling Problem. International Journal of Advanced ManufacturingTechnology,2008,39:965-974.
[134] Gao L, Zhang GH, Zhang LP, Li XY, An Efficient Memetic Algorithm for Solvingthe Job Shop Scheduling Problem. Computers&Industrial Engineering,2011,60:699-705.
[135] Shafaei R. Brunn P. The Performance of Heuristic Scheduling Rules in A DynamicJob Shop Environment Using A Rolling Time Horizon Approach. InternationalJournal of Production Research,1999,37:3913–3925.
[136] Vinod V, Sridharan R. Dynamic Job-Shop Scheduling with Sequence-DependentSetup Times: Simulation Modeling and Analysis. International Journal of AdvancedManufacturing Technology,2008,36(3-4):355-372.
[137] Liu SQ, Ong HL, Ng KM. A Fast Tabu Search Algorithm for the Group ShopScheduling Problem. Advances in Engineering Software,2005,36:533-539.
[138] Kutanoglu E., Sabuncuoglu I., Routing-Based Reactive Scheduling Policies forMachine Failures in Dynamic Job Shops, International Journal of ProductionResearch,2001.39:14,3141-3158.
[139] Vinod V., Sridharan R., Simulation-Based Metamodels for Scheduling A DynamicJob Shop with Sequence-Dependent Setup Times, International Journal of ProductionResearch.2009.47(6),1425-1447.
[140] Kashan A.H., Karimi B., Jolai F., An Effective Hybrid Multi-Objective GeneticAlgorithm for Bi-Criteria Scheduling on A Single Batch Processing Machine withNon-Identical Job Sizes. Engineering Applications of Artificial Intelligence,2010.23,911-922.
[141] Wang L., Zheng D.Z. An Effective Hybrid Optimization Strategy for Job-ShopScheduling Problems. Computers&Operations Research,2001.28,585-590.
[142] Pinedo M., Scheduling Theory, Algorithms, and Systems, Prentice-Hall, EnglewoodCliffs, NJ (Chapter2).2002.
[143] Zhang C.Y., Li P.G., Guan Z.L., Rao Y. Q., A Tabu Search Algorithm with A NewNeighbourhood Structure for The Job Shop Scheduling Problem. Computers&Operations Research,2007.34(11),3229-3242.
[144] Nowicki E., Smutnicki C., A Fast Taboo Search Algorithm for the Job Shop Problem.Management Science,1996.42(6),797-813.
[145] P., Fattahi, M. S., Mehrabad, F. Jolai, Mathematical Modeling and HeuristicApproaches To Flexible Job Shop Scheduling Problems, Journal of IntelligentManufacturing,2007.18(3):331–342.
[146] P., Moscato, M. G. Norman, A Memetic Approach for The Traveling SalesmanProblem Implementation of A Computational Ecology for CombinatorialOptimization on Message-Passing Systems, Parallel Computing and TransputerApplications,1992,1:177–186.
[147] Ronald S. Robust Eneodings in Genetic Algorithms: A Survey of Eneoding Issues.IEEE International Confereneeon Evolutionary Computation.1997.43-48.
[148]王微，林剑艺，崔胜辉，等.碳足迹分析方法研究综述.环境科学与技术.2010(7):71-78.
[149] Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb. Multi-Objective EvolutionaryOptimisation for Product Design and Manufacturing. Springer London DordrechtHeidelberg New York,2011:279-304.
[150] U.S. Energy Information Administration. International Energy Outlook2011.DOE/EIA-0484,2011.
[151]刘飞，徐宗俊，但斌.机械加工系统能量特性及其应用，北京:机械工业出版社，1995
[152] Gutowski T., Thermodynamic Analysis of Resources Used in ManufacturingProcesses，Environmental Science and Technology,2009,43:1584-1590.
[153] Donaldh O, John A. An Integrated Analysis of Policies That Increase Investments inAdvanced Energy Efficient Low Carbon Technologies. Energy Economics,2004,26(4):739-755.
[154] Kojism, Yoshitaka T, kei G, et al. Developing a Long Term Local Society DesignMethodology towards a Low Carbon Economy: An Application to Shiga Prefecturein Japan. Energy Policy,2007,35(9):4688-4703.
[155] Ishibuchi, H., Murata, T. A Multi-Objective Genetic Local Search Algorithm and ItsApplication to Flowshop Scheduling. IEEE Transactions on Systems, Man, andCybernetics—Part C: Applications and Reviews,1998.28(3),392–403.
[156] Liping Zhang, Xinyu Li, Liang Gao, Guohui Zhang, Xiaoyu Wen. DynamicScheduling Model in FMS by Considering Energy Consumption and ScheduleEfficiency. The201216th IEEE International Conference on Computer SupportedCooperative Work in Design (CSCWD2012)., Wuhan China, May23-25,2012.
[157] Sabuncuoglu I, Goren S Hedging Production Schedules Against Uncertainty inManufacturing Environment with A Review of Robustness and Stability Research. IntJ Comput Integr Manuf,2009,22(2):138-157.
[2]聂黎.基于GEP的车间动态调度方法与技术研究:[博士学位论文].武汉:华中科技大学,2011.
[3]齐二石，孔海宁，何曙光.钢铁企业副产煤气多周期动态优化调度.系统工程理论与实践.2010(11):2071-2079.
[4]熊禾根.模具企业动态车间作业计划研究:[博士学位论文].武汉:华中科技大学,2005.
[5] Jackson J. R., Simulation Research on Job Shop Production. Naval Research LogisticsQuarterly,1957.4(3):287-295.
[6] Ramash R. Dynamic Job Shop Scheduling--A Review of Simulation Research.OMEGA,1990,18(1):43-57.
[7] Szelde Elizabeth Kerr, Roger M. Knowledge-Based Reactive Scheduling. ProductionPlanning&Control,1994.5(5):124-145.
[8] Suresh V., Chandhuri D. Dynamic Scheduling--A Survey of Research. InternationalJournal of Production Economics,1993.32(1):53-63.
[9] Vieira G. E., Hermann, J. W., Lin, E. Rescheduling Manufacturing Systems: AFramework of Strategies, Policies and Methods. Journal of Scheduling.2003.6(1),36-92.
[10] Aytug H, Lawley MA, Mckay K, et al. Executing Production Schedules in the Face ofUncertainties: A Review and Some Future Directions.5th International Conferenceon Industrial Engineering and Production Management (IEPM01), Aug20-24,2001,Quebec City, Canada. European Journal of Operational Research,2005,161(1):86-110.
[11] Potts CN, Strusevich VA. Fifty Years of Scheduling: A Survey of Milestones. Journalof the Operational Research Society,2009,60, S41--S68.
[12] Ouelhadj D., Petrovic S., A Survey of Dynamic Scheduling in ManufacturingSystems. Journal of Scheduling,2009.12,417-431.
[13] Mehta S. V., Uzsoy R. Predictable Scheduling of a Single Machine Subject toBreakdowns. International Journal of Computer Integrated Manufacturing,1999.12(1),15-38.
[14] Vieira G.E., Herrmann J.W., Lin E. Analytical Models to Predict the Performance of aSingle Machine System under Periodic and Event-Driven Rescheduling Strategies.International Journal of Production Research,2000.38(8),1899-1915.
[15] Renna P., Job Shop Scheduling by Pheromone Approach in a Dynamic Environment.International Journal of Computer Integrated Manufacturing,2010.23(5),412-424.
[16]钱晓龙,唐立新,刘文新.动态调度的研究方法综述,.控制与决策.2001,16(2):141-145.
[17] Leon V.J. Wu S.D. Storer R.H. Robustness Measures and Robust Scheduling for JobShops. IIE Transactions,1994,26(5),32-41.
[18] Cowling P. I. Johansson M. Using Real-Time Information for Effective DynamicScheduling. European Journal of Operational Research.2002,139(2):230-244.
[19] Ruedee Rangsaritratsamee, William G. Ferrell Jr., Mary Beth Kurz. DynamicRescheduling that Simultaneously Considers Efficiency and Stability. Computers&Industrial Engineering,2004,46:1-15.
[20] Abumaizar, R.J., J.A. Svestka. Rescheduling Job Shops under Random Disruptions.International Journal of Production Research,1997.35(7):2065-2082.
[21]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.
[22]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,(18):2190-2197.
[23] Jian A K, Elmaraghy H A. Production Scheduling Rescheduling in FlexibleManufacturing. International Journal of Production Research,1997,35(1):281-309.
[24] Tay J C, Ho N B. Evolving Dispatching Rules Using Genetic AlgorithmProgramming for Solving Multi-Objective Flexible Job Shop Problem. Computer&Industrial engineering,2008,54(3):453-473.
[25] Lawrence SR, Sewell EC. Heuristic, Optimal, Static and Dynamic Schedules whenProcessing Times are Uncertain. Journal of Operations Management,1997,15(1):71-82.
[26] Sanjay V. Mehta., Reha M. Uzsoy. Predictable Scheduling of a Job Shop Subject toBreakdowns. IEEE Transactions on robotics and automation,1998,14(3):365-378.
[27] O'Donovan, R., R. Uzsoy., K.N. McKay. Predictable Scheduling of a Single Machinewith Breakdowns and Sensitive Jobs. International Journal of Production Research.1999,37(18):4217-4233.
[28]李素粉,朱云龙,尹朝万.具有随机加工时间和机器故障的流水车间调度.计算机集成制造系统,2005,11(10):1245-1249.
[29]刘琳,谷寒雨,席裕庚.加工时间不确定的Just-in-Time单机鲁棒调度.2007,22(10):1151-11541159.
[30] Sabuncuoglu I., Bayiz M. Analysis of Reactive Scheduling Problems in a Job ShopEnvironment. European Journal of Operational Research.2000,126(3):567-586.
[31] Church LK, Uzsoy R. Analysis of Periodic and Event-Driven Rescheduling Policiesin Dynamic Shops. International Journal of Computer Integrated Manufacturing,1992,5(3):153-163.
[32]方剑，席裕庚.基于事件驱动的job shop仿真调度系统.系统仿真学报.1997.12:42-50.
[33]王冰，席裕庚，谷寒雨.一类单机动态调度问题的改进滚动时域方法.控制与决策.2005,20(3):257-260.
[34]刘越洋，席裕庚.基于两步滚动的单机调度算法研究.计算机工程.2004,30(24).144-146164.
[35] Sabuncuoglu I, Karabuk S. Rescheduling Frequency in an FMS with UncertainProcessing Times and Unreliable Machines. Journal of Manufacturing Systems,1999,18(4):268-281.
[36]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.
[37]吴秀丽.多目标柔性作业车间调度技术研究:[博士学位论文].西安：西北工业大学.2006.
[38]张国辉.柔性作业车间调度方法研究:[博士学位论文].武汉：华中科技大学,2009.
[39]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].武汉：华中科技大学,2009.
[40]鞠全勇，朱剑英.基于混合遗传算法的动态车间调度系统的研究.中国机械工程.2007,18(1):40-43.
[41]胡咏梅，贾磊，李歧强.一种基于动态粗集的工件滚动调度识别方法.机械工程学报.2005,41(3).67-71.
[42]刘明周，单晖，蒋增强，葛茂根,扈静,张铭鑫.不确定条件下车间动态重调度优化方法.机械工程学报.2009,45(10).137-142.
[43]王晓娟.多目标柔性作业车间调度方法研究:[博士学位论文].武汉：华中科技大学,2011.
[44] Leus, R., Herroelen, W. The complexity of Machine Scheduling for Stability with aSingle Disrupted Job. Operations Research Letters,2005.33(2),151-156.
[45] Wong T N,Leung C W, Mak K L, et al. Integrated Process Planning andScheduling/Rescheduling–an Agent-based Approach. International Journal ofProduction Research,2006,44(18-19):3627-3655.
[46] Ouelhadj, D., Cowling, P. I., Petrovic, S. Utility and Stability Measures forAgent-Based Dynamic Scheduling of Steel Continuous Casting. In Proceedings ofthe IEEE International Conference on Robotics and Automation. Taipei, Taiwan.2003.175-180.
[47] Daniels, R. L., Kouvelis, P. Robust Scheduling to Hedge Against Processing TimeUncertainty in Single-Stage Production. Management Science,1995.41(2),363–737.
[48] Jensen, M. T. Improving Robustness and Flexibility of Tardiness and TotalFlow-Time Job Shops Using Robustness Measures. Applied Soft Computing,2001,1(1),35–52.
[49] Fattahi P, Fallahi A. Dynamic Scheduling in Flexible Job Shop Systems byConsidering Simultaneously Efficiency and Stability. CIRP Journal of ManufacturingScience and Technology.2010,2(2):114-123.
[50] Sun, J., Xue, D. A Dynamic Reactive Scheduling Mechanism for Responding toChanges of Production Orders and Manufacturing Resources. Computers in Industry,2001,46(2),189–207.
[51] Dorn, J., Kerr, R. M., Thalhammer, G. Reactive Scheduling: Improving theRobustness of Schedules and Restricting the Effects of Shop Floor Disturbances byFuzzy Reasoning. International Journal of Human Computer Studies,1995,42,687–704.
[52]丁雷,王爱民,宁汝新.工时不确定条件下的车间作业调度技术.计算机集成制造系统,2010.16(1):98-108.
[53]黄振刚，鲁建厦，王成. MES环境下作业车间多级动态调度方法研究.机械设计与制造.2011(11):240-242.
[54] Liping Zhang, Liang Gao, Xinyu Li. A Hybrid Algorithm for Job Shop SchedulingProblems with Unexpected Disruptions, International Journal of AdvancedManufacturing Technology,2012, DOI:10.1007/s00170-012-4245-6.
[55] Goren S., Sabuncuoglu I. Robustness and Stability Measures for Scheduling:Single-Machine Environment. IIE Transactions,2008.40(1):66-83.
[56] Matsuura H., Tsubone H., Kanezashi M. Sequencing, Dispatching and Switching in aDynamic Manufacturing Environment. International Journal of Production Research,1993.31(7):1671-1688.
[57] Holloway C. A., Nelson R. T. Job Shop Scheduling with Due Dates and VariableProcessing Times. Management Science,1974.20,1264-1275.
[58] Pierreval H., Mebarki N., Dynamic Scheduling Selection of Dispatching Rules forManufacturing System. International Journal of Production Research,1997.35(6),1575-1591.
[59] Rajendran C., Holthaus O., A Comparative Study of Dispatching Rules in DynamicFlowshops and Jobshops. European Journal of Operational Research,1999.116,156-170.
[60] Subramaniam V., Lee G.K., Ramesh T., Hong G.S., Wong Y.S., Machine SelectionRules in A Dynamic Job Shop. Internal Journal of Advanced ManufacturingTechnology,2000.16,902-908.
[61]方剑,席裕庚,周期性和时间驱动的Job Shop滚动调度策略.控制与决策,1997.12(2):159-162166.
[62] Dominic, P.D.D., Kaliyamoorthy S., Kumar M.S., Efficient Dispatching Rules forDynamic Job Shop Scheduling. Internal Journal of Advanced ManufacturingTechnology,2004.24,70-75.
[63]易平，李建军，熊禾根.基于多级优先规则的模具企业动态车间作业调度算法,模具工业.2009,35(5):7-9.
[64] Alpay S, Yuzugullu N., Dynamic Job Shop Scheduling for Missed Due DatePerformance. International Journal of Production Research,2009.47(15),4047-4062.
[65] Nie L., Shao X.Y., Gao L., Li W., Evolving Scheduling Rules with Gene ExpressionProgramming for Dynamic Single-Machine Scheduling Problems. InternationalJournal of Advanced Manufacturing Technology,2010.50,729-747.
[66]聂黎，高亮，胡译丹.基于前序基因表达式编程的单机成组调度算法.计算机集成制造系统.2007,13(11):2261-2275.
[67]李鹏，刘民，吴澄.一类并行机调度问题的动态调度算法.计算机集成制造系统.2007(3):568-572.
[68]刘晓冰，李忠凯，黄学文,宋清杰.一种面向柔性生产的动态调度模型.计算机应用研究.2009(9):3239-3241.
[69] Branke J, Mattfeld D C. Anticipation and Flexibility in Dynamic Scheduling.International journal of production research.2005,43(15):3103-3129.
[70] Chryssolouris G., Subramaniam V. Dynamic Scheduling of Manufacturing Job ShopsUsing Genetic Algorithms. Journal of Intelligent Manufacturing,2001,12,281-293.
[71]刘爱军，杨育，邢青松，等.柔性作业车间多目标动态调度.计算机集成制造系统.2011(12):2629-2637.
[72]胡咏梅,基于粗集的车间动态调度研究:[博士学位论文],青岛:山东大学博士学位论文.2004.
[73] Liu S.Q., Ong H.L., Ng. K. M. Metaheuristics for Minimizing the Makespan of TheDynamic Shop Scheduling Problem. Advances in Engineering Software,2005,36,199-205.
[74] Liu S.Q. Ong H.L. Metaheuristics for the Mixed Shop Scheduling. Asia-PacificJournal of Operational Research (APJOR),2004.21(1):97-115.
[75] Malve S., Uzsoy R. A Genetic Algorithm for Minimizing Maximum Lateness onParallel Identical Batch Processing Machines with Dynamic Job Arrivals andIncompatible Job Families. Computers&Operations Research,2007.34,3016-3028.
[76] Zhou R., Nee A. Y. C., Lee H. P. Performance of an Ant Colony OptimizationAlgorithm in Dynamic Job Shop Scheduling Problems. International Journal ofProduction Research,2009,47(11):2903-2920.
[77] Zhou, R., H.P. Lee, A.Y.C. Nee, Applying Ant Colony Optimisation (ACO) Algorithmto Dynamic Job Shop Scheduling Problems. International Journal of ManufacturingResearch.20083(3):301-320.
[78] Adibi, M.A., Zandieh M., Amiri M., Multi-Objective Scheduling of Dynamic JobShop Using Variable Neighborhood Search. Expert Systems with Applications,2010.37(1),282-287.
[79] Zandieh M., Adibi M.A., Dynamic Job Shop Scheduling Using VariableNeighbourhood Search. International Journal of Production Research,2010.48(8),2449-2459.
[80]王策,王书锋,冯冬青,梁燕.混合粒子群算法在Job-Shop动态调度中的应用.计算机工程与应用.201046(26):219-222.
[81]魏英姿，谷侃锋.基于性能预测的遗传强化学习动态调度方法.系统仿真学报.2010(12):2809-2812.
[82]杨红红,吴智铭.基于自适应遗传算法的柔性动态调度研究.中国机械工程,2002,13(21):1845-1848.
[83]余建军,孙树栋,王军强等.免疫模拟退火算法及其在柔性动态Job Shop中的应用.中国机械工程,2007,18(7):793-799.
[84] Dewan P., Joshi S. Auction-Based Distributed Scheduling in a Dynamic Job ShopEnvironment. International Journal of Production Research,2002.40(5):1173-1191.
[85]张晴,饶运清,车间动态调度方法研究.机械制造,2003.41(461):39-41.
[86] Xiang, W. H.P. Lee, Ant Colony Intelligence in Multi-Agent Dynamic ManufacturingScheduling. Engineering Applications of Artificial Intelligence,2008.21(1):73-85
[87]徐新黎,郝平,王万良.多Agent动态调度方法在染色车间调度中的应用.计算机集成制造系统.2010,16(3):611-620.
[88] Rajabinasab A, Mansour S. Dynamic Flexible Job Shop Scheduling with AlternativeProcess Plans: an Agent-Based Approach. International Journal of AdvancedManufacturing Technology.2011,54(9-12):1091-1107.
[89] Nejad, H.T.N., N. Sugimura. Iwamura, Agent-Based Dynamic Integrated ProcessPlanning and Scheduling in Flexible Manufacturing Systems. International Journal ofProduction Research,2011.49(5):1373-1389.
[90] Kouiss Khalid, Pierreval Henri, Nasser Mebarki. Using Multi-Agent Architecture inFMS for Dynamic Scheduling. Journal of Intelligent Manufacturing,1997,8(1):41-47.
[91]宋娟.多Agent分布式车间动态调度仿真系统研究.制造业自动化.2011(24):57-58.
[92]王国磊，林琳，钟诗胜.基于模糊聚类的Q-学习在动态调度中的应用.计算机集成制造系统.2009(4):751-757.
[93] Ramasesh R. Dynamic Job Shop Scheduling: A Survey of Simulation Research.Omega,1990.18(1):43-57.
[94] P Priore, D De La Fuente, A Gomez, J Puente. A Review of Machine Learning inDynamic Scheduling of Flexible Manufacturing Systems. AI EDAM,2001.15(3):251-263.
[95] Priore, P., Furnte D., Puente J., Parreno J. A Comparison of Machine-LearningAlgorithms for Dynamic Scheduling of Flexible Manufacturing Systems.Engineering Applications of Artificial Intelligence,2006.19(3):247-255.
[96] IMC26. Energy Efficient&Low Carbon Manufacturing, Sept. Dublinhttp://internationalmanufacturingconference.com/Location.php,2009.
[97] S A Melnyk, R P Sroufe, F L Montabon, T J Hinds. Green MRP: Identifying theMaterial and Environmental Impacts of Production Schedules. International Journalof Production Research,2001,39(8):1559-1573.
[98] STC C2009Annual report. The Scientific Technical Committee Cutting-CIRP,2009
[99]路甬祥.走向绿色和智能制造——中国制造发展之路.中国机械工程.2010(4):379-386.
[100]唐德才，宋平，李长顺.中国制造业低碳发展现状研究.产业与科技论坛.2011(21):5-7.
[101]刘清涛，蔡宗琰，刘晓婷，等.一种面向绿色制造的多目标车间调度方法.长安大学学报(自然科学版).2011(2):106-110.
[102]唐任仲，贾顺，吕景祥.基于活动的产品车间制造过程碳排量计算方法.机电工程.2011(6):639-644.
[103]刘献礼，陈涛.机械制造中的低碳制造理论与技术.哈尔滨理工大学学报.2011(1):1-8.
[104]何彦.面向绿色制造的机械加工系统任务优化调度方法研究:[博士学位论文].重庆：重庆大学,2007.
[105]刘飞，李聪波，曹华军，等.基于产品生命周期主线的绿色制造技术内涵及技术体系框架.机械工程学报.2009(12):115-120.
[106]李聪波，刘飞，谭显春，等.基于风险矩阵和模糊集的绿色制造实施风险评估方法.计算机集成制造系统.2010(1):209-214.
[107]刘飞，曹华军.绿色制造的理论体系框架.中国机械工程.2000(9):10-13.
[108]谭显春，刘飞，曹华军，等.绿色制造的一种工艺路线决策模型及其求解算法.机械工程学报.2004(4):154-159.
[109]谭显春，刘飞，曹华军，等.面向绿色制造的刀具选择决策模型及其应用.重庆大学学报(自然科学版).2003(3):117-121.
[110]何彦，刘飞，曹华军，等.面向绿色制造的工艺规划支持系统及应用.计算机集成制造系统.2005(7):975-980.
[111]何彦，刘飞，曹华军，等.面向绿色制造的机械加工系统任务优化调度模型.机械工程学报.2007(4):27-33.
[112]王永靖，刘飞，王琦峰.面向绿色制造的汽车制造企业信息系统及关键技术研究.中国机械工程.2009(7):832-837.
[113]曹华军，刘飞，何彦.机械加工系统节能降噪型综合任务分配模型及其应用.机械工程学报.2006(5):97-102.
[114] He Y, Liu F, Cao H J, et al. A Bi-Objective Model for Job-Shop Scheduling Problemto Minimize Both Energy Consumption and Makespan. Journal of Central SouthUniversity of Technology.2005,122:167-171.
[115] He Y, Liu F. Methods for Integrating Energy Consumption and EnvironmentalImpact Considerations into the Production Operation of Machining Processes.Chinese Journal of Mechanical Engineering.2010,23(4):428-435.
[116] Industrial Assessment Centers. http://www1.eere.energy.gov/industry/best practices/iacs. html,2009.
[117] Gong Yunqi, Lv Min, Wang Gang, et.al, Calculation and Prediction Of EnergyConsumption in Discrete Manufactures Based on Knowledge,14th InternationalConference on Industrial Engineering and Engineering Management, Tianjin, China,OCT20-22,2007.
[118] Gutowski T, Murphy C, Allen D, etc. Environmentally Benign Manufacturing:Observations from Japan, Europe and the United States. Journal of CleanerProduction,2005,13:1-17.
[119] Dietmair A, Verl A. A Generic Energy Consumption Model for Decision Making andEnergy Efficiency Optimization in Manufacturing. International Journal ofSustainable Engineering,2009,2(2):123-133.
[120] Dietmair A, Verl A. Energy Consumption Forecasting and Optimisation for ToolMachines. Modern Machinery Science Journal,2009(3):62-67
[121] Bauer D J, Thurwachter S, Sheng P S. Integration of Environmental Factors inSurface Planning: Part1--Mass and Energy Modeling, Transactions of NAMRI/SME,1998,26:171-176
[122] Cai Y P, Huang G H, Lin Q G, et al. An Optimization-Model-Based InteractiveDecision Support System for Regional Energy Management Systems Planning UnderUncertainty. Expert Systems with Applications,2009,36:3470-3482.
[123] Rockwell Automation. Extending the Scope of MES to Achieve Energy Efficiencyand Sustainability Goals. Publication SUST-WP001A-EN-P, May2009.
[124] Herrmann C, Thiede S. Process Chain Simulation to Foster Energy Efficiency inManufacturing. CIRP Journal of Manufacturing Science and Technology,2009,1:221-229
[125]张超勇.基于自然算法的作业车间调度问题理论与应用研究:[博士学位论文].武汉:华中科技大学,2006.
[126] Van Hulle MM. A Goal Programming Network for Mixed Integer LinearProgramming: A Case Study for the Job-Shop Scheduling Problem. InternationalJournal of Neural Systems,1991,2(3):201-209
[127]刘琳.动态不确定环境下生产调度算法研究:[博士学位论文].上海:上海交通大学,2007.
[128] Bierwirth C. Mattfeld D.C. Production Scheduling and Rescheduling with GeneticAlgorithms. Evolutionary Computation,1999,7(1):1-17
[129] Church L.K. and Uzsoy R. Analysis of Periodic and Event-Driven ReschedulingPolicies in Dynamic Shops. International Journal of Computer IntegratedManufacturing,1992,5(3):153-163
[130] Turkcan A., Akturk M. S., Storer R.H. Predictive/Reactive Scheduling withControllable Processing Times and Earliness-Tardiness Penalties. IIE Transactions,2009.41(12),1080-1095.
[131] Vinod V., Sridharan R. Simulation Modeling and Analysis of Due-Date AssignmentMethods and Scheduling Decision Rules in A Dynamic Job Shop Production System.International Journal of Production Economics,2011.129(1):127-146.
[132] Park BJ, Choi HR, Kim HS. A Hybrid Genetic Algorithm for the Job ShopScheduling Problems. Computers&Industrial Engineering,2003,45(4):597–613.
[133] Zhang, C.Y., Rao, Y.Q. Li, P.G. An Effective Hybrid Genetic Algorithm for the JobShop Scheduling Problem. International Journal of Advanced ManufacturingTechnology,2008,39:965-974.
[134] Gao L, Zhang GH, Zhang LP, Li XY, An Efficient Memetic Algorithm for Solvingthe Job Shop Scheduling Problem. Computers&Industrial Engineering,2011,60:699-705.
[135] Shafaei R. Brunn P. The Performance of Heuristic Scheduling Rules in A DynamicJob Shop Environment Using A Rolling Time Horizon Approach. InternationalJournal of Production Research,1999,37:3913–3925.
[136] Vinod V, Sridharan R. Dynamic Job-Shop Scheduling with Sequence-DependentSetup Times: Simulation Modeling and Analysis. International Journal of AdvancedManufacturing Technology,2008,36(3-4):355-372.
[137] Liu SQ, Ong HL, Ng KM. A Fast Tabu Search Algorithm for the Group ShopScheduling Problem. Advances in Engineering Software,2005,36:533-539.
[138] Kutanoglu E., Sabuncuoglu I., Routing-Based Reactive Scheduling Policies forMachine Failures in Dynamic Job Shops, International Journal of ProductionResearch,2001.39:14,3141-3158.
[139] Vinod V., Sridharan R., Simulation-Based Metamodels for Scheduling A DynamicJob Shop with Sequence-Dependent Setup Times, International Journal of ProductionResearch.2009.47(6),1425-1447.
[140] Kashan A.H., Karimi B., Jolai F., An Effective Hybrid Multi-Objective GeneticAlgorithm for Bi-Criteria Scheduling on A Single Batch Processing Machine withNon-Identical Job Sizes. Engineering Applications of Artificial Intelligence,2010.23,911-922.
[141] Wang L., Zheng D.Z. An Effective Hybrid Optimization Strategy for Job-ShopScheduling Problems. Computers&Operations Research,2001.28,585-590.
[142] Pinedo M., Scheduling Theory, Algorithms, and Systems, Prentice-Hall, EnglewoodCliffs, NJ (Chapter2).2002.
[143] Zhang C.Y., Li P.G., Guan Z.L., Rao Y. Q., A Tabu Search Algorithm with A NewNeighbourhood Structure for The Job Shop Scheduling Problem. Computers&Operations Research,2007.34(11),3229-3242.
[144] Nowicki E., Smutnicki C., A Fast Taboo Search Algorithm for the Job Shop Problem.Management Science,1996.42(6),797-813.
[145] P., Fattahi, M. S., Mehrabad, F. Jolai, Mathematical Modeling and HeuristicApproaches To Flexible Job Shop Scheduling Problems, Journal of IntelligentManufacturing,2007.18(3):331–342.
[146] P., Moscato, M. G. Norman, A Memetic Approach for The Traveling SalesmanProblem Implementation of A Computational Ecology for CombinatorialOptimization on Message-Passing Systems, Parallel Computing and TransputerApplications,1992,1:177–186.
[147] Ronald S. Robust Eneodings in Genetic Algorithms: A Survey of Eneoding Issues.IEEE International Confereneeon Evolutionary Computation.1997.43-48.
[148]王微，林剑艺，崔胜辉，等.碳足迹分析方法研究综述.环境科学与技术.2010(7):71-78.
[149] Lihui Wang, Amos H. C. Ng, Kalyanmoy Deb. Multi-Objective EvolutionaryOptimisation for Product Design and Manufacturing. Springer London DordrechtHeidelberg New York,2011:279-304.
[150] U.S. Energy Information Administration. International Energy Outlook2011.DOE/EIA-0484,2011.
[151]刘飞，徐宗俊，但斌.机械加工系统能量特性及其应用，北京:机械工业出版社，1995
[152] Gutowski T., Thermodynamic Analysis of Resources Used in ManufacturingProcesses，Environmental Science and Technology,2009,43:1584-1590.
[153] Donaldh O, John A. An Integrated Analysis of Policies That Increase Investments inAdvanced Energy Efficient Low Carbon Technologies. Energy Economics,2004,26(4):739-755.
[154] Kojism, Yoshitaka T, kei G, et al. Developing a Long Term Local Society DesignMethodology towards a Low Carbon Economy: An Application to Shiga Prefecturein Japan. Energy Policy,2007,35(9):4688-4703.
[155] Ishibuchi, H., Murata, T. A Multi-Objective Genetic Local Search Algorithm and ItsApplication to Flowshop Scheduling. IEEE Transactions on Systems, Man, andCybernetics—Part C: Applications and Reviews,1998.28(3),392–403.
[156] Liping Zhang, Xinyu Li, Liang Gao, Guohui Zhang, Xiaoyu Wen. DynamicScheduling Model in FMS by Considering Energy Consumption and ScheduleEfficiency. The201216th IEEE International Conference on Computer SupportedCooperative Work in Design (CSCWD2012)., Wuhan China, May23-25,2012.
[157] Sabuncuoglu I, Goren S Hedging Production Schedules Against Uncertainty inManufacturing Environment with A Review of Robustness and Stability Research. IntJ Comput Integr Manuf,2009,22(2):138-157.