汽车4S店维修服务系统动态调度
|
摘要
中国目前已成为世界第一大汽车市场。但是,中国的汽车产业在品牌、研发、销售和服务等各个环节上与世界汽车强国都还存在差距。研究汽车4S店维修服务系统的动态调度问题对汽车行业和具体的汽车销售企业都有价值。此外,这方面的研究成果还可以推广应用到其他服务系统。论文首先分析了汽车4S店维修服务系统调度问题的特征,将其描述为存在明显瓶颈环节的动态FJSP问题；接着,结合约束理论和系统分解思想简化该复杂系统的调度问题。研究采取由简至繁的策略,分三个层次：首先,解决瓶颈资源的多目标动态调度问题；接着,在此基础上构建单瓶颈环境下汽车4S店维修服务系统动态调度策略；然后,扩展至多约束环境下汽车4S店维修服务系统动态调度。通过描述以上各自问题的特征与性质,建立问题的数学模型,并且有针对性地选择、改进和组合多种调度算法与动态调度策略,实现问题求解。主要研究内容如下：
第1章首先介绍了课题研究的理论意义和现实价值,进而分析了服务系统动态调度问题的研究现状,从中提炼出课题的具体研究对象——汽车4S店维修服务系统调度问题的几点解决办法。接着,结合对基于约束理论的调度问题和调度算法研究现状的总结,指出了本文的技术路线和主要研究工作。
第2章首先介绍了约束理论的核心理念和管理技术；接着针对本课题的具体研究对象,对问题描述涉及的相关参数进行说明,并运用三元组α/β/γ方法,采用对比的方式,对汽车4S店维修服务系统与制造系统调度问题的共性和汽车4S店维修服务系统调度问题的特性进行描述；最后提出基于约束理论的汽车4S店维修服务系统动态调度问题分解框架,提出后续研究的总体思路。
第3章结合优化调度理论,对汽车维修车间瓶颈工序(油漆工序)调度问题的特性、模型和求解算法进行了研究,提出了多目标动态调度策略。首先,从最小化目标、机器环境、加工特征和约束几方面分析了油漆工序调度问题的特征,建立了对应的数学模型。接着,结合混合重调度策略和两阶段算法,设计了动态调度方法。然后,分别运用遗传算法、粒子群算法和改进模拟植物生长算法,在问题初始解的基础上,进行了第二阶段迭代求解。最后,以实例分析比较了几种算法的性能,实验仿真结果表明改进模拟植物生长算法对相关参数的要求宽松,稳定性好,可以在缩短求解时间的同时确保解的质量,有效地提高了瓶颈环节调度的效率和质量,从而提升了汽车4S点维修服务系统的工作效率和服务质量。
第4章研究了单瓶颈环境下汽车4S店维修服务系统动态调度问题。首先,结合瓶颈资源的多目标动态调度的结果,有针对性地选择合适的重调度策略和分派规则调度非瓶颈资源；接着,在瓶颈和非瓶颈资源之间设置缓冲,进行缓冲管理,建立瓶颈和非瓶颈资源调度方案的协调机制,实现单瓶颈环境下的服务系统动态调度问题求解；最后,以日常任务量下的汽车4S店维修车间调度问题为实例验证算法,相对于企业中使用到的一些调度规则,基于约束理论调度算法的仿真结果显著优越,且随着问题规模的增大,指标性能的优越性更为明显,从而大大减少了客户的等待时间。
第5章研究了多约束机环境下汽车4S店维修服务系统的动态调度问题。首先,对多约束机环境下汽车4S店维修服务系统的调度问题进行描述,建立数学模型,并构建基于约束理论的多约束机环境下系统动态调度问题的分解框架。接着,结合汽车维修服务系统中不同任务的权重存在差异的特征,通过改进约束引导的启发式算法求解多约束机的调度问题；然后,完善约束机与非约束机之间的协调机制,实现多约束环境下的服务系统动态调度问题求解；最后,以高峰期任务量下的汽车4S店维修车间调度问题为实例验证算法,结果表明该算法优于企业中常用的调度规则算法,有效地缩短了客户的等待时间。
第6章结合某汽车4S店维修服务的具体案例,运用论文中的方法进行了作业排序资源调度。首先,介绍了汽车的售后维修保养服务流程和该企业维修车间的情况。接着,以该公司高峰期情况下一天的具体任务作为数据基础,运用论文所提方法进行了排序,所得调度方案优于企业实际的调度结果。最后,针对调度的重点和难点,提出了几点对应的管理策略建议。
结论部分对整个论文的主要成果进行了总结,指出论文研究的创新之处,并对本课题未来的研究工作进行了展望与设想。
China has already become No.1auto market in the world. However, on the aspects of auto brand, research and development, sales and service, etc, there are gaps between and among China's automobile industry and that of the automobile superpower nations. Study on the dynamic scheduling problem of automobile4S shop's repair service system has value both in automobile industry and specific auto sales business. In addition, the results can also be applied to other service systems. Firstly, it analyzed the characteristics of the automobile4S shop's repair service system. Then, it described this problem as a dynamic FJSP problem with obvious bottleneck. At last, it simplified the scheduling problem of the complex system combined with the theory of constraints and the thought of system-decomposing. The research was broken down into three levels:Firstly, it solved the multi-objective dynamic scheduling problem of bottleneck resource in the4S shop; secondly, based on the first level, it built the dynamic scheduling strategy of the4S shop on the condition of single bottleneck; and thirdly it extended up to the multi-objective dynamic scheduling problem on the condition of multi-constraint. The study adopts the strategy of from simple to complex. Through the description of their characteristics and natures, it established a mathematical model, and targeted combination of scheduling algorithm and dynamic scheduling strategy. The main contents are shown as follows:
Chapter1introduces the research's theoretical meaning and practical value, and then it analyzes the status quo of the service system's dynamic scheduling problem, from which to extract the specific object of study subjects-several solutions of automobile4S shop's repair service system scheduling problem. Then, point out the paper's technical line and the main research work combined with the summary of the status quo.
Chapter2introduces the core concepts and management techniques of theory of constraints; then explain the problem description related parameters of the specific study object and use triple of α/β/γ method and comparative approach to describe the similarity of the scheduling problem between4S shop and manufacturing systems, including the characteristic of the first one; Finally, put up with the decomposition framework of the4S shop repair service system's dynamic scheduling problem which based on the constraint theory and the general idea of future research.
Chapter3combined with the optimal scheduling theory makes a study on the workshop's bottleneck operation (painting process) scheduling problem's characteristic, models, and solution problem, puts up with the multi-objective scheduling strategy. First, analyze the feature of painting operation scheduling problem from the aspects of the minimized the objective, machine environment, processing characteristics and constraints, and establish the corresponding mathematical model. Second, design the dynamic scheduling algorithm with the combination of hybrid rescheduling strategy and two-stage algorithm. Third, use genetic algorithms, particle swarm optimization and improvement of plant growth simulation algorithm respectively to go on the second stage of getting the iterative solution on basis of the initial solution of this problem. Finally, use real examples to analyze and compare the performance of algorithms. Experimental results show that the improved plant growth simulation algorithm in the calculation of the time and the quality of reconciliation is superior.
Chapter4makes a study on the dynamic scheduling problem of the4S shop's repair service system under the single-bottleneck environment. First of all, select the appropriate dispatch rules and re-scheduling policy to schedule the non-bottleneck resource on the basis of bottleneck resource's multi-objective scheduling results; Then, set the buffer between the bottleneck and non-bottleneck resources, manage the buffer, and establish coordination mechanisms of the bottleneck and non-bottleneck resource, to achieve the solution of services system's dynamic scheduling problem in the environment of single-bottleneck; Finally, verify the algorithm through the4S shop's repair service workshop's scheduling problem with the routing task.
Chapter5makes a study on the4S shop repair system's dynamic scheduling problem under the multi-constraint environment. First, describe the problem, build the mathematical model, and establish the problem's decomposed framework on the basis of theory of constraints. Then, solve this problem through the improving constraint guided heuristic algorithm; improve the coordination mechanism between the constrained machines and non-constrained machines, and realize the solution of service system dynamic scheduling problem under the multi-constraint environment. Finally, take the4S shop repair service workshop with the peaking assignment as an example to verify the algorithm.
Chapter6uses its algorithm to schedule the operations and resources in a specific4S shop repair service workshop. First of all, introduce automobile's after-sales maintenance services and the situation of the maintenance workshop. Then, use the proposed method to schedule the problem with the peaking day's data which will get a better scheduling scheme than the actual results in the shop. Finally, for the important and difficult scheduling points, propose the corresponding management strategy recommendations.
Conclusion summarizes the main results of the paper, points out the innovation, and looks forward this subject's future work.
第1章首先介绍了课题研究的理论意义和现实价值,进而分析了服务系统动态调度问题的研究现状,从中提炼出课题的具体研究对象——汽车4S店维修服务系统调度问题的几点解决办法。接着,结合对基于约束理论的调度问题和调度算法研究现状的总结,指出了本文的技术路线和主要研究工作。
第2章首先介绍了约束理论的核心理念和管理技术；接着针对本课题的具体研究对象,对问题描述涉及的相关参数进行说明,并运用三元组α/β/γ方法,采用对比的方式,对汽车4S店维修服务系统与制造系统调度问题的共性和汽车4S店维修服务系统调度问题的特性进行描述；最后提出基于约束理论的汽车4S店维修服务系统动态调度问题分解框架,提出后续研究的总体思路。
第3章结合优化调度理论,对汽车维修车间瓶颈工序(油漆工序)调度问题的特性、模型和求解算法进行了研究,提出了多目标动态调度策略。首先,从最小化目标、机器环境、加工特征和约束几方面分析了油漆工序调度问题的特征,建立了对应的数学模型。接着,结合混合重调度策略和两阶段算法,设计了动态调度方法。然后,分别运用遗传算法、粒子群算法和改进模拟植物生长算法,在问题初始解的基础上,进行了第二阶段迭代求解。最后,以实例分析比较了几种算法的性能,实验仿真结果表明改进模拟植物生长算法对相关参数的要求宽松,稳定性好,可以在缩短求解时间的同时确保解的质量,有效地提高了瓶颈环节调度的效率和质量,从而提升了汽车4S点维修服务系统的工作效率和服务质量。
第4章研究了单瓶颈环境下汽车4S店维修服务系统动态调度问题。首先,结合瓶颈资源的多目标动态调度的结果,有针对性地选择合适的重调度策略和分派规则调度非瓶颈资源；接着,在瓶颈和非瓶颈资源之间设置缓冲,进行缓冲管理,建立瓶颈和非瓶颈资源调度方案的协调机制,实现单瓶颈环境下的服务系统动态调度问题求解；最后,以日常任务量下的汽车4S店维修车间调度问题为实例验证算法,相对于企业中使用到的一些调度规则,基于约束理论调度算法的仿真结果显著优越,且随着问题规模的增大,指标性能的优越性更为明显,从而大大减少了客户的等待时间。
第5章研究了多约束机环境下汽车4S店维修服务系统的动态调度问题。首先,对多约束机环境下汽车4S店维修服务系统的调度问题进行描述,建立数学模型,并构建基于约束理论的多约束机环境下系统动态调度问题的分解框架。接着,结合汽车维修服务系统中不同任务的权重存在差异的特征,通过改进约束引导的启发式算法求解多约束机的调度问题；然后,完善约束机与非约束机之间的协调机制,实现多约束环境下的服务系统动态调度问题求解；最后,以高峰期任务量下的汽车4S店维修车间调度问题为实例验证算法,结果表明该算法优于企业中常用的调度规则算法,有效地缩短了客户的等待时间。
第6章结合某汽车4S店维修服务的具体案例,运用论文中的方法进行了作业排序资源调度。首先,介绍了汽车的售后维修保养服务流程和该企业维修车间的情况。接着,以该公司高峰期情况下一天的具体任务作为数据基础,运用论文所提方法进行了排序,所得调度方案优于企业实际的调度结果。最后,针对调度的重点和难点,提出了几点对应的管理策略建议。
结论部分对整个论文的主要成果进行了总结,指出论文研究的创新之处,并对本课题未来的研究工作进行了展望与设想。
China has already become No.1auto market in the world. However, on the aspects of auto brand, research and development, sales and service, etc, there are gaps between and among China's automobile industry and that of the automobile superpower nations. Study on the dynamic scheduling problem of automobile4S shop's repair service system has value both in automobile industry and specific auto sales business. In addition, the results can also be applied to other service systems. Firstly, it analyzed the characteristics of the automobile4S shop's repair service system. Then, it described this problem as a dynamic FJSP problem with obvious bottleneck. At last, it simplified the scheduling problem of the complex system combined with the theory of constraints and the thought of system-decomposing. The research was broken down into three levels:Firstly, it solved the multi-objective dynamic scheduling problem of bottleneck resource in the4S shop; secondly, based on the first level, it built the dynamic scheduling strategy of the4S shop on the condition of single bottleneck; and thirdly it extended up to the multi-objective dynamic scheduling problem on the condition of multi-constraint. The study adopts the strategy of from simple to complex. Through the description of their characteristics and natures, it established a mathematical model, and targeted combination of scheduling algorithm and dynamic scheduling strategy. The main contents are shown as follows:
Chapter1introduces the research's theoretical meaning and practical value, and then it analyzes the status quo of the service system's dynamic scheduling problem, from which to extract the specific object of study subjects-several solutions of automobile4S shop's repair service system scheduling problem. Then, point out the paper's technical line and the main research work combined with the summary of the status quo.
Chapter2introduces the core concepts and management techniques of theory of constraints; then explain the problem description related parameters of the specific study object and use triple of α/β/γ method and comparative approach to describe the similarity of the scheduling problem between4S shop and manufacturing systems, including the characteristic of the first one; Finally, put up with the decomposition framework of the4S shop repair service system's dynamic scheduling problem which based on the constraint theory and the general idea of future research.
Chapter3combined with the optimal scheduling theory makes a study on the workshop's bottleneck operation (painting process) scheduling problem's characteristic, models, and solution problem, puts up with the multi-objective scheduling strategy. First, analyze the feature of painting operation scheduling problem from the aspects of the minimized the objective, machine environment, processing characteristics and constraints, and establish the corresponding mathematical model. Second, design the dynamic scheduling algorithm with the combination of hybrid rescheduling strategy and two-stage algorithm. Third, use genetic algorithms, particle swarm optimization and improvement of plant growth simulation algorithm respectively to go on the second stage of getting the iterative solution on basis of the initial solution of this problem. Finally, use real examples to analyze and compare the performance of algorithms. Experimental results show that the improved plant growth simulation algorithm in the calculation of the time and the quality of reconciliation is superior.
Chapter4makes a study on the dynamic scheduling problem of the4S shop's repair service system under the single-bottleneck environment. First of all, select the appropriate dispatch rules and re-scheduling policy to schedule the non-bottleneck resource on the basis of bottleneck resource's multi-objective scheduling results; Then, set the buffer between the bottleneck and non-bottleneck resources, manage the buffer, and establish coordination mechanisms of the bottleneck and non-bottleneck resource, to achieve the solution of services system's dynamic scheduling problem in the environment of single-bottleneck; Finally, verify the algorithm through the4S shop's repair service workshop's scheduling problem with the routing task.
Chapter5makes a study on the4S shop repair system's dynamic scheduling problem under the multi-constraint environment. First, describe the problem, build the mathematical model, and establish the problem's decomposed framework on the basis of theory of constraints. Then, solve this problem through the improving constraint guided heuristic algorithm; improve the coordination mechanism between the constrained machines and non-constrained machines, and realize the solution of service system dynamic scheduling problem under the multi-constraint environment. Finally, take the4S shop repair service workshop with the peaking assignment as an example to verify the algorithm.
Chapter6uses its algorithm to schedule the operations and resources in a specific4S shop repair service workshop. First of all, introduce automobile's after-sales maintenance services and the situation of the maintenance workshop. Then, use the proposed method to schedule the problem with the peaking day's data which will get a better scheduling scheme than the actual results in the shop. Finally, for the important and difficult scheduling points, propose the corresponding management strategy recommendations.
Conclusion summarizes the main results of the paper, points out the innovation, and looks forward this subject's future work.
引文
[1]Adams J., Balas E., Zawack D., The shifting bottleneck procedure for job shop scheduling[J].Management Science,1988,34(3):391-401.
[2]Al-Anzi F S, Allahverdi A. A self-adaptive differential evolution heuristic for two-stage assembly scheduling problem to minimize maximum lateness with setup times[J]. European Journal of Operational Research,2007,182(10):80-94.
[3]Anglani A., Grieco A., Guerriero E., Musmanno R. Robust scheduling of parallel machines with sequence-dependent set-up costs[J]. European Journal of Operational Research,2005,161(3):704-720.
[4]Artigues C., Billaut J.C., Esswein. C. Maximization of solution flexibility for robust shop scheduling[J]. European Journal of Operational Research,2005,165(9):314-328.
[5]Artigues C., Michelon P., Reusser S. Insertion techniques for static and dynamic resource constrained project scheduling[J].European Journal of Operational Research, 2003,149(9):249-267.
[6]Augusto. V., Xie. X., Perdomo. V. Operating theatre scheduling with patient recovery in both operating rooms and recovery beds[J]. Computers & Industrial Engineering,2010, 58(3):231-238.
[7]Bagheri. A., Zandieh. M., Mahdavi.I., Yazdani. M. An artificial immune algorithm for the flexible job-shop scheduling problem[J]. Future Generation Computer Systems, 2010,26(4):533-541.
[8]Balas E., Lenstra J.K., Vazacopoulos A., The one-machine problem with delayed precedence constraints and its use in job shop scheduling[J].Management Science,1995, 41(1):94-109.
[9]Baskan. O., Haldenbilen. S., Ceylan. H., Ceylan.H. A new solution algorithm for improving performance of ant colony optimization[J]. Applied Mathematics and Computation.2009,211(5):75-84.
[10]Betterton.C.E, Cox III. J.F. Espoused drum-buffer-rope flow control in serial lines:A comparative study of simulation models[J]. International Journal of Production Economics.2009.1 (1).66-79.
[11]Briskorn. D., Drexl. A. A branching scheme for finding cost-minimal round robin tournaments[J]. European Journal of Operational Research.2009,197(8):68-76.
[12]Bruker P., Schlie R., Job-shop scheduling with multipurpose machines[J]. Computing, 1990,45(4):369-375.
[13]Cai W, Yang W W, Chen X Q. A global optimization algorithm based on plant growth theory: Plant growth optimization[C]. Intelligent Computation Technology and Automation, Hunan,2008:1194-1199.
[14]Cardoen.B, Demeulemeester.E, Belien.J. Sequencing surgical cases in a day-care environment:An exact branch-and-price approach[J]. Computers & Operations Research.2009,36(9):2660-2669.
[15]Caumond. A., Lacomme. P., Moukrim. A., Tchernev. N. An MILP for scheduling problems in an FMS with one vehicle[J]. European Journal of Operational Research.2009,199(12):706-722.
[16]Chen.Chun-Lung, Chen. Chuen-Lung. A bottleneck-based heuristic for minimizing makespan in a flexible flow line with unrelated parallel machines[J]. Computers & Operations Research.2009,36(2).3073-3081.
[17]Chiang S.Y., Kuo C.T., Meerkov S.M., C-Bottlenecks in serial production lines:identification and application[C]. Decision and Control, Phoenix,2001:543-578.
[18]Chtourou H., Haouari M. A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling[J]. Computers & Industrial Engineering,2008, 55 (8):183-194.
[19]Dauzere-Peres S., Lasserre J.B., A modified shifting bottleneck procedure for job-shop scheduling[J]. International Journal of Production Research,1993,31:923-932.
[20]Demirkol E., Mehta S., Uzsoy R., A computational study of shifting bottleneck procedures for shop scheduling problems[J]. Journal of Heuristic,1997,3(2):111-137.
[21]Fei. H., Meskens. N., Chu. C. A planning and scheduling problem for an operating theatre using an open scheduling strategy[J]. Computers & Industrial Engineering.2010, 58(3):221-230.
[22]Gao.J., Gen.M, Sun.L., Zhao.X. A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems[J]. Computers & Industrial Engineering.2007,53(8):149-162.
[23]Gao. J. Sun. L., Gen. M. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems[J]. Computers & Operations Research.2008, 35(9):2892-2907.
[24]Gen. M., Lin. L., Zhang. H. Evolutionary techniques for optimization problems in integrated manufacturing system:State-of-the-art-survey[J]. Computers & Industrial Engineering.2009,56(3):779-808.
[25]Giffler B., Thompson G.L., Algorithms for solving production scheduling problems[J]. Operations Research,1960,8:487-503.
[26]Goldratt E.M., Cox J., The Goal: a Process of Ongoing Improvement, Croton-on-Hudson[M]. NY:North River Press,1984:1-100.
[27]Goldratt E.M., Shifting information out of the data ocean:the haystack syndrome, Croton-on-Hudson[M]. NY: North River Press,1990:3-286.
[28]Gong. H, Tang. L., Duin. C.W. A two-stage flow shop scheduling problem on a batching machine and a discrete machine with blocking and shared setup times[J]. Computers & Operations Research.2010,37(5):960-969.
[29]Graham R.L., Lawler E.L., Lenstra J.K., Rinnooy K.A.H.G., Optimization and approximation in deterministic sequencing and scheduling:A survey[J]. Annals of Discrete Mathematics,1979,5:287-326.
[30]Guney K, Durmus A, Basbug S. A plant growth simulation algorithm for pattern nulling of linear antenna arrays by amplitude control[J]. Progress In Electromagnetics Research B,2009,17:69-84.
[31]Hans.E., Wullink.G, Houdenhoven. M., Kazemier.G. Robust surgery loading[J]. European Journal of Operational Research.2008,185(3):1038-1050.
[32]Hendizadeh S H, Faramarzi H, Mansouri S A, Gupta J N D, ElMekkawy T Y. Meta-heuristics for scheduling a flowline manufacturing cell with sequence dependent family setup times[J].International Journal of Production Economics,2008,111 (2): 593-605.
[33]Hota. P.K., Barisal. A.K., Chakrabarti. R. An improved PSO technique for short-term optimal hydrothermal scheduling[J].Electric Power Systems Research.2009, 79(7):1047-1053.
[34]Holland J H. Adaptation in natural and artificial system[M]. Ann Arbor. University of Michigan Press,1975:6-177.
[35]http://www.APICS.org/sibs/CM/TOC.htm.
[36]http://www.chinanews.com.cn/auto/2010/11-04/2635257.shtml.
[37]http://www.chinesetax.com.cn/caishuiwenku/hongguaniingji/maoyijingji/200711/50519 40.html.
[38]http://www.cnstock.com/papernew/html/2010-05/31/content24242.htm.
[39]http://www.gov.cn/jrzg/2010-09/05/content1696112.htm.
[40]http://www.gov.cn/jrzg/2011-01/10/content1781693.htm.
[41]IBM Almaden Services Research. Service Science, Management and Engineering (SSME) [R]. IBM Corporation,2007:1-98.
[42]Irnich. S. A new branch-and-price algorithm for the traveling tournament problem[J]. European Journal of Operational Research.2010.204(7):218-228.
[43]Jain A.S., Meeran S., Deterministic job-shop scheduling:past, present and future[J]. European Journal of Operational Research,1999,113(3):390-434.
[44]Jensen M.T. Finding worst-case flexible schedules using co evolution[C]. In:Proc. of the Genetic and Evolutionary Computation Conference. Spector, L.et al. (Ed.). GECCO 2001, July 7-11, San Francisco, USA:2001:1144-1151.
[45]Kennedy J, Eberhart R C. Particle swarm optimization[A]. Proceedings of IEEE International Conference on Neutral Networks[C].Piscatwang, NY, USA:IEEE Service Center,1995.1942-1948.
[46]Knust. S, Lucking. D. Minimizing costs in round robin tournaments with place constraints [J]. Computers & Operations Research.2009.36(11):2937-2943.
[47]Knust. S. Scheduling sports tournaments on a single court minimizing waiting times[J]. Operations Research Letters.2008.36(7):471-476.
[48]Kuo-Jung Y., Sheng-Hung C., Rong-Kwei Li. Enhancement of theory of constraints replenishment using a novel generic buffer management procedure[J].International journal of production research,2003,41(4):725-740.
[49]Lawrence S.R., Buss A.H., Shifting production bottlenecks:causes,cures,and conundrums[J]. Journal of Production and Operations Management,1994,3(3):21-37.
[50]Lee. C, Qi. X. Vehicle scheduling with combinable delivery and pickup operations[J]. Operations Research Letters.2009.37(11):399-404.
[51]Lee.G.C., Kim.Y.D., Choi.S.W..Bottleneck-focused scheduling for a hybrid flow shop[J]. International Journal of Production Research.2004.42:165-81.
[52]Lee L.H., Lee C.U., Tan Y.P. A multi-objective genetic algorithm for robust flight scheduling using simulation[J]. European Journal of Operational Research,2007, (177):1948-1968.
[53]Lee Y.H., Bhaskaran K., Pinedo M.. A heuristic to minimize the total weighted tardiness with seqence-dependent setups[J]. IIETransactions,1997, (29),45-52.
[54]Leung. C.W., Wong. T.N., Mak. K.L., Fung. R.Y.K. Integrated process planning and scheduling by an agent-based ant colony optimization[J]. Computers & Industrial Engineering.2010.59(8):166-180.
[55]Liu. B., Wang. L, Jin. Y.H. An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers[J]. Computers & Operations Research.2008.35(9):2791-2806.
[56]Mason S.J., Fowler J.W., Carlyle W.M., A modified shifting bottleneck heuristic for minimizing total weighted tardiness in complex job shops[J]. Journal of Scheduling, 2002,5:247-262.
[57]Mason S.J., Fowler J.W., Carlyle W.M., Montgomery D.C., Heuristics for minimizing total weighted tardiness in complex job shops[J]. International Journal of Production Research,2005,43:1943-1963.
[58]Mason S.J., Jin S., Wessels C.M., Rescheduling strategies for minimizing total weighted tardiness in complex job shops[J].International Journal of Production Research,2004, 42:613-628.
[59]Mehta S.V., Uzsoy R. Predictable scheduling of a job shop subject to breakdowns[J]. IEEE Transactions on Robotics and Automation,1998,14(3):365-378.
[60]Mehta S.V., Uzsoy R. Predictable scheduling of a single machine subject to breakdowns[J]. International Journal of Computer Integrated Manufacturing,1999, 12(1):15-38.
[61]Newmann M E J. The structure and function of complex networks[J]. SIAM Rev,2003, (45):167-256.
[62]Panwalkar S.S., Iskander W., A survey of scheduling rules[J]. Operations Research, 1977,25:45-61.
[63]Pezzella.F., Morganti.G., Ciaschetti.G. A genetic algorithm for the Flexible Job-shop Scheduling Problem[J].Computers & Operations Research.2008.35(10):3202-3212.
[64]Pinedo M., Planning and Scheduling in Manufacturing and Services [M]. New York, Springer science and business media,2005:205-319.
[65]Pinedo M., Scheduling:Theory, algorithms, and systems (2nd Edition) [M]. New Jersey, Prentice Hall,2002:333-391.
[66]Pinedo M., Singer M., A shifting bottleneck heuristic for minimizing the total weighted tardiness in a Job shop[J]. Naval Research Logistics,1999,46:1-17.
[67]Rao R. S, Narasimham S V L. Optimal capacitor placement in a radial distribution system using plant growth simulation algorithm [J]. International Journal of Electrical Power and Energy and Energy Systems Engineering.2008,1 (2):123-130.
[68]Roland.B., Martinelly.C.D, Riane.F., Pochet.Y. Scheduling an operating theatre under human resource constraints[J]. Computers & Industrial Engineering.2010.58(3): 212-220.
[69]Roser C., Nakano M., Tanaka M.,Shifting bottleneck detection[C]. Proceedings of the 2002 Winter Simulation Conference,2002:121-134.
[70]Ruiz-Torres A J, Lopez F J, Ho J C. Scheduling uniform parallel machines subject to a secondary resource to minimize the number of tardy jobs[J]. European Journal of Operational Research,2007,(179):302-315.
[71]Sabuncuoglu I., Bayiz M. Analysis of reactive scheduling problems in a Job shop environment[J]. European Journal of Operational Research,2000,126(3):567-586.
[72]Sadi A.A.Causes of delay in large construction project[J]. Journal of Project management,2006, (24):349-357.
[73]Seckiner. S.U., Gokcen. H., Kurt. M. An integer programming model for hierarchical workforce scheduling problem[J].European Journal of Operational Research. 2007.183(12):694-699.
[74]Sha. D.Y., Lin. H.H. A multi-objective PSO for job-shop scheduling problems[J]. Expert Systems with Applications.2010.37(3):1065-1070.
[75]Shafaei R., Brunn P. Workshop scheduling using practical(inaccurate)data-Part 2:Aninvestigation of the robustness of scheduling rules in a dynamic and stochastic environment[J]. International Journal of Production Research,1999,37(18),4105-4117.
[76]Sheen G J, Liao L W. Scheduling machine-dependent jobs to minimize lateness on machines with identical speed under availability constraints[J]. Computers & Operations Research,2007,(34):2266-2278.
[77]Simons Jr J.V., Simpson III W.P., An exposition of multiple constraint scheduling as implemented in the goal system (formerly DISASTERTM) [J]. Production and Operations Management,1997,6:3-22.
[78]Simons Jr J.V., Simpson III W.P., Carlson B.J., James S.W., Lettiere C.A., Mediate Jr B.A., Formulation and solution of the drum-buffer-rope constraint scheduling problem (DBRCSP) [J]. International Journal of Production Research,1996,34:2405-2420.
[79]Simons Jr J.V., Stephens M.D., Simpson III W.P., Simultaneous versus sequential scheduling of multiple resources which constrain system throughput[J]. International Journal of Production Research,1999,37:21-33.
[80]Spohrer.J, Maglio.P. Emergence of service science:services sciences, management, engineering (SSME) as the next frontier in innovation[EB/OL].(2005209208) [2006210210]. http://www.ibm.com/ibm/governmentalprogrames/SSME.pdf.
[81]Stolletz. R. Operational workforce planning for check-in counters at airports[J]. Transportation Research Part E:Logistics and Transportation Review.2010.46(5): 414-425.
[82]Sun J., Xue D. A dynamic reactive scheduling mechanism for responding to changes of production orders and manufacturing resources[J]. Computers in Industry,2001,46(2): 189-207.
[83]Tang H B, Ye C M. Application of plant growth simulation algorithm for job shop scheduling[C]. Advanced Management Science (ICAMS). Chengdu.2010:568-572.
[84]Uzsoy R., Wang C.S., Performance of decomposition procedures for Job shop scheduling problems with bottleneck machines[J]. International Journal of Production Research,2000,38:1271-1286.
[85]Valls. V., Perez. A., Quintanilla. S. Skilled workforce scheduling in Service Centres[J]. European Journal of Operational Research.2009.193(3):791-804.
[86]Vepsalainen A.P.J., Morton T.E., Priority rules for job shops with weighted tardiness costs[J]. Management Science,1987,33:1035-1047.
[87]Vieira G.E., Herrmann J.W., Lin E. Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies[J]. International Journal of Production Research,2000,38(8):1899-1915.
[88]Volgenant.A., Duin.C.W.. Improved polynomial algorithms for robust bottleneck problems with interval data[J]. Computers & Operations Research.2010.37(3).909-915.
[89]Wang CH, Cheng H ZH. Transmission network optimal planning based on plant growth simulation algorithm[J]. European Transactions on Electrical Power,2009,19 (2) 291-301.
[90]Yan. L., Song. B.J., Feng. H.X., Ye.J. A heuristic project scheduling approach for quick response to maritime disaster rescue[J]. International Journal of Project Management. 2009.27(8):620-628.
[91]Yazdani. M., Amiri. M., Zandieh. M. Flexible job-shop scheduling with parallel variable neighborhood search algorithm[J]. Expert Systems with Applications.2010.37(1):678-687.
[92]Zhang. F, Cao. X, Yang. D. Intelligent Scheduling of Public Traffic Vehicles Based on a Hybrid Genetic Algorithm[J] Tsinghua Science & Technology.2008.13(5):625-631.
[93]Zhang. G, Shao. X, Li. P., Gao. L. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J]. Computers & Industrial Engineering,2009.56(5):1309-1318.
[94]Zuo.Y, G.H, Xi.. Modified bottleneck-based heuristic for large-scale job-shop scheduling problems with a bottleneck[J]. Journal of Systems Engineering and Electronics.2007.18:556-565.
[95]陈旭梅,林国鑫,于雷.常规公共交通与轨道交通运营调度协调模型[J].系统工程理论与实践.2009.29(10)：165-173.
[96]董红宇,黄敏,王兴伟,郑秉霖.一体化炼钢炉次计划模型与算法[J].系统仿真学报.2009.21(8)：4801-4805.
[97]郭重庆.服务科学_一个极具前沿意义的学科[J].中国科学基金.2008.(4)：217-220.
[98]郭永辉Drum-Buffer-Rope理论研究综述[J].工业工程与管理.2008.(2)：1-4.
[99]李敬花.遗传蚁群融合算法求解多项目资源能力平衡问题[J].计算机集成制造系统.2010.12(2)：183-187.
[100]李彤,王春峰,王文波,宿伟玲.求解整数规划的一种仿生类全局优化算法——模拟植物生长算法[J].系统工程理论与实践,2005.1(1)：76-85.
[101]李彤,王众托.模拟植物生长算法与知识创新的几点思考[J].管理科学学报,2010,13(3)：87-96.
[102]李彤,王众托.模拟植物生长算法在设施选址问题中的应用[J].系统工程理论与实践,2008,28(]2)：107-115.
[103]李妍峰,李军,赵达.基于迭代局域搜索的智能优化算法求解车辆调度问题研究[J].系统工程理论与实践,2007(5)：75-81
[104]刘民,吴澄,尹文龙.带特殊工艺约束的并行机器生产线调度问题的一种遗传算法[J].自动化学报,2001,27(3)：381-386.
[105]刘士新.项目优化调度理论与方法[M].北京：机械工业出版社,2007：1-42.
[106]刘志雄,王少梅.基于粒子群算法的并行多机调度问题研究[J].计算机集成制造系统,2006,12(2)：183-187
[107]刘作仪,杜少甫.服务科学管理与工程：一个正在兴起的领域[J].管理学报,2008,5(7)：607-614
[108]苗东升.系统科学精要[M].北京：中国人民大学出版社,1998：1-67.
[109]彭武良,王成恩.关键链项目调度模型及遗传算法求解[J].系统工程学报,2010,25(2)：123-131.
[110]涂序彦,王枞,郭燕慧.大系统控制论[M].北京：北京邮电大学出版社,2005：1-58.
[111]王东生,曹磊.混沌、分形及其应用[M].合肥：中国科学技术大学出版社,1995：1-93.
[112]王凌.车间调度及其遗传算法[M].北京：清华大学出版社,2003：1-102.
[113]王谦,陈芳莲.多系统服务中心的多技能服务人员调度问题研究[J].系统科学与数学,2008,28(11)：1337-1345.
[114]王玉荣.瓶颈管理[M].北京：机械工业出版社,2002：1-149.
[115]吴斌,蔡红,樊树海,蒋南云.双倍体差分进化粒子群算法在VRPSDP中的应用研 究[J].系统工程理论与实践,2010,30(3)：520-526.
[116]席裕庚.动态大系统方法导论[M].北京：国防工业出版社,1988：1-72.
[117]肖人彬,陶振武.群集智能研究进展[J].管理科学学报,2007,10(6)：80-91.
[118]张龙,刘民,刘涛,吴澄.复杂产品开发项目调度问题的模糊优化算法[J].控制工程,2009,16(11)：731-734.
[119]左燕,谷寒雨,席裕庚.大规模流水线调度的瓶颈分解算法研究[J].控制与决策,2006,21(4)：425-429.
[2]Al-Anzi F S, Allahverdi A. A self-adaptive differential evolution heuristic for two-stage assembly scheduling problem to minimize maximum lateness with setup times[J]. European Journal of Operational Research,2007,182(10):80-94.
[3]Anglani A., Grieco A., Guerriero E., Musmanno R. Robust scheduling of parallel machines with sequence-dependent set-up costs[J]. European Journal of Operational Research,2005,161(3):704-720.
[4]Artigues C., Billaut J.C., Esswein. C. Maximization of solution flexibility for robust shop scheduling[J]. European Journal of Operational Research,2005,165(9):314-328.
[5]Artigues C., Michelon P., Reusser S. Insertion techniques for static and dynamic resource constrained project scheduling[J].European Journal of Operational Research, 2003,149(9):249-267.
[6]Augusto. V., Xie. X., Perdomo. V. Operating theatre scheduling with patient recovery in both operating rooms and recovery beds[J]. Computers & Industrial Engineering,2010, 58(3):231-238.
[7]Bagheri. A., Zandieh. M., Mahdavi.I., Yazdani. M. An artificial immune algorithm for the flexible job-shop scheduling problem[J]. Future Generation Computer Systems, 2010,26(4):533-541.
[8]Balas E., Lenstra J.K., Vazacopoulos A., The one-machine problem with delayed precedence constraints and its use in job shop scheduling[J].Management Science,1995, 41(1):94-109.
[9]Baskan. O., Haldenbilen. S., Ceylan. H., Ceylan.H. A new solution algorithm for improving performance of ant colony optimization[J]. Applied Mathematics and Computation.2009,211(5):75-84.
[10]Betterton.C.E, Cox III. J.F. Espoused drum-buffer-rope flow control in serial lines:A comparative study of simulation models[J]. International Journal of Production Economics.2009.1 (1).66-79.
[11]Briskorn. D., Drexl. A. A branching scheme for finding cost-minimal round robin tournaments[J]. European Journal of Operational Research.2009,197(8):68-76.
[12]Bruker P., Schlie R., Job-shop scheduling with multipurpose machines[J]. Computing, 1990,45(4):369-375.
[13]Cai W, Yang W W, Chen X Q. A global optimization algorithm based on plant growth theory: Plant growth optimization[C]. Intelligent Computation Technology and Automation, Hunan,2008:1194-1199.
[14]Cardoen.B, Demeulemeester.E, Belien.J. Sequencing surgical cases in a day-care environment:An exact branch-and-price approach[J]. Computers & Operations Research.2009,36(9):2660-2669.
[15]Caumond. A., Lacomme. P., Moukrim. A., Tchernev. N. An MILP for scheduling problems in an FMS with one vehicle[J]. European Journal of Operational Research.2009,199(12):706-722.
[16]Chen.Chun-Lung, Chen. Chuen-Lung. A bottleneck-based heuristic for minimizing makespan in a flexible flow line with unrelated parallel machines[J]. Computers & Operations Research.2009,36(2).3073-3081.
[17]Chiang S.Y., Kuo C.T., Meerkov S.M., C-Bottlenecks in serial production lines:identification and application[C]. Decision and Control, Phoenix,2001:543-578.
[18]Chtourou H., Haouari M. A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling[J]. Computers & Industrial Engineering,2008, 55 (8):183-194.
[19]Dauzere-Peres S., Lasserre J.B., A modified shifting bottleneck procedure for job-shop scheduling[J]. International Journal of Production Research,1993,31:923-932.
[20]Demirkol E., Mehta S., Uzsoy R., A computational study of shifting bottleneck procedures for shop scheduling problems[J]. Journal of Heuristic,1997,3(2):111-137.
[21]Fei. H., Meskens. N., Chu. C. A planning and scheduling problem for an operating theatre using an open scheduling strategy[J]. Computers & Industrial Engineering.2010, 58(3):221-230.
[22]Gao.J., Gen.M, Sun.L., Zhao.X. A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems[J]. Computers & Industrial Engineering.2007,53(8):149-162.
[23]Gao. J. Sun. L., Gen. M. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems[J]. Computers & Operations Research.2008, 35(9):2892-2907.
[24]Gen. M., Lin. L., Zhang. H. Evolutionary techniques for optimization problems in integrated manufacturing system:State-of-the-art-survey[J]. Computers & Industrial Engineering.2009,56(3):779-808.
[25]Giffler B., Thompson G.L., Algorithms for solving production scheduling problems[J]. Operations Research,1960,8:487-503.
[26]Goldratt E.M., Cox J., The Goal: a Process of Ongoing Improvement, Croton-on-Hudson[M]. NY:North River Press,1984:1-100.
[27]Goldratt E.M., Shifting information out of the data ocean:the haystack syndrome, Croton-on-Hudson[M]. NY: North River Press,1990:3-286.
[28]Gong. H, Tang. L., Duin. C.W. A two-stage flow shop scheduling problem on a batching machine and a discrete machine with blocking and shared setup times[J]. Computers & Operations Research.2010,37(5):960-969.
[29]Graham R.L., Lawler E.L., Lenstra J.K., Rinnooy K.A.H.G., Optimization and approximation in deterministic sequencing and scheduling:A survey[J]. Annals of Discrete Mathematics,1979,5:287-326.
[30]Guney K, Durmus A, Basbug S. A plant growth simulation algorithm for pattern nulling of linear antenna arrays by amplitude control[J]. Progress In Electromagnetics Research B,2009,17:69-84.
[31]Hans.E., Wullink.G, Houdenhoven. M., Kazemier.G. Robust surgery loading[J]. European Journal of Operational Research.2008,185(3):1038-1050.
[32]Hendizadeh S H, Faramarzi H, Mansouri S A, Gupta J N D, ElMekkawy T Y. Meta-heuristics for scheduling a flowline manufacturing cell with sequence dependent family setup times[J].International Journal of Production Economics,2008,111 (2): 593-605.
[33]Hota. P.K., Barisal. A.K., Chakrabarti. R. An improved PSO technique for short-term optimal hydrothermal scheduling[J].Electric Power Systems Research.2009, 79(7):1047-1053.
[34]Holland J H. Adaptation in natural and artificial system[M]. Ann Arbor. University of Michigan Press,1975:6-177.
[35]http://www.APICS.org/sibs/CM/TOC.htm.
[36]http://www.chinanews.com.cn/auto/2010/11-04/2635257.shtml.
[37]http://www.chinesetax.com.cn/caishuiwenku/hongguaniingji/maoyijingji/200711/50519 40.html.
[38]http://www.cnstock.com/papernew/html/2010-05/31/content24242.htm.
[39]http://www.gov.cn/jrzg/2010-09/05/content1696112.htm.
[40]http://www.gov.cn/jrzg/2011-01/10/content1781693.htm.
[41]IBM Almaden Services Research. Service Science, Management and Engineering (SSME) [R]. IBM Corporation,2007:1-98.
[42]Irnich. S. A new branch-and-price algorithm for the traveling tournament problem[J]. European Journal of Operational Research.2010.204(7):218-228.
[43]Jain A.S., Meeran S., Deterministic job-shop scheduling:past, present and future[J]. European Journal of Operational Research,1999,113(3):390-434.
[44]Jensen M.T. Finding worst-case flexible schedules using co evolution[C]. In:Proc. of the Genetic and Evolutionary Computation Conference. Spector, L.et al. (Ed.). GECCO 2001, July 7-11, San Francisco, USA:2001:1144-1151.
[45]Kennedy J, Eberhart R C. Particle swarm optimization[A]. Proceedings of IEEE International Conference on Neutral Networks[C].Piscatwang, NY, USA:IEEE Service Center,1995.1942-1948.
[46]Knust. S, Lucking. D. Minimizing costs in round robin tournaments with place constraints [J]. Computers & Operations Research.2009.36(11):2937-2943.
[47]Knust. S. Scheduling sports tournaments on a single court minimizing waiting times[J]. Operations Research Letters.2008.36(7):471-476.
[48]Kuo-Jung Y., Sheng-Hung C., Rong-Kwei Li. Enhancement of theory of constraints replenishment using a novel generic buffer management procedure[J].International journal of production research,2003,41(4):725-740.
[49]Lawrence S.R., Buss A.H., Shifting production bottlenecks:causes,cures,and conundrums[J]. Journal of Production and Operations Management,1994,3(3):21-37.
[50]Lee. C, Qi. X. Vehicle scheduling with combinable delivery and pickup operations[J]. Operations Research Letters.2009.37(11):399-404.
[51]Lee.G.C., Kim.Y.D., Choi.S.W..Bottleneck-focused scheduling for a hybrid flow shop[J]. International Journal of Production Research.2004.42:165-81.
[52]Lee L.H., Lee C.U., Tan Y.P. A multi-objective genetic algorithm for robust flight scheduling using simulation[J]. European Journal of Operational Research,2007, (177):1948-1968.
[53]Lee Y.H., Bhaskaran K., Pinedo M.. A heuristic to minimize the total weighted tardiness with seqence-dependent setups[J]. IIETransactions,1997, (29),45-52.
[54]Leung. C.W., Wong. T.N., Mak. K.L., Fung. R.Y.K. Integrated process planning and scheduling by an agent-based ant colony optimization[J]. Computers & Industrial Engineering.2010.59(8):166-180.
[55]Liu. B., Wang. L, Jin. Y.H. An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers[J]. Computers & Operations Research.2008.35(9):2791-2806.
[56]Mason S.J., Fowler J.W., Carlyle W.M., A modified shifting bottleneck heuristic for minimizing total weighted tardiness in complex job shops[J]. Journal of Scheduling, 2002,5:247-262.
[57]Mason S.J., Fowler J.W., Carlyle W.M., Montgomery D.C., Heuristics for minimizing total weighted tardiness in complex job shops[J]. International Journal of Production Research,2005,43:1943-1963.
[58]Mason S.J., Jin S., Wessels C.M., Rescheduling strategies for minimizing total weighted tardiness in complex job shops[J].International Journal of Production Research,2004, 42:613-628.
[59]Mehta S.V., Uzsoy R. Predictable scheduling of a job shop subject to breakdowns[J]. IEEE Transactions on Robotics and Automation,1998,14(3):365-378.
[60]Mehta S.V., Uzsoy R. Predictable scheduling of a single machine subject to breakdowns[J]. International Journal of Computer Integrated Manufacturing,1999, 12(1):15-38.
[61]Newmann M E J. The structure and function of complex networks[J]. SIAM Rev,2003, (45):167-256.
[62]Panwalkar S.S., Iskander W., A survey of scheduling rules[J]. Operations Research, 1977,25:45-61.
[63]Pezzella.F., Morganti.G., Ciaschetti.G. A genetic algorithm for the Flexible Job-shop Scheduling Problem[J].Computers & Operations Research.2008.35(10):3202-3212.
[64]Pinedo M., Planning and Scheduling in Manufacturing and Services [M]. New York, Springer science and business media,2005:205-319.
[65]Pinedo M., Scheduling:Theory, algorithms, and systems (2nd Edition) [M]. New Jersey, Prentice Hall,2002:333-391.
[66]Pinedo M., Singer M., A shifting bottleneck heuristic for minimizing the total weighted tardiness in a Job shop[J]. Naval Research Logistics,1999,46:1-17.
[67]Rao R. S, Narasimham S V L. Optimal capacitor placement in a radial distribution system using plant growth simulation algorithm [J]. International Journal of Electrical Power and Energy and Energy Systems Engineering.2008,1 (2):123-130.
[68]Roland.B., Martinelly.C.D, Riane.F., Pochet.Y. Scheduling an operating theatre under human resource constraints[J]. Computers & Industrial Engineering.2010.58(3): 212-220.
[69]Roser C., Nakano M., Tanaka M.,Shifting bottleneck detection[C]. Proceedings of the 2002 Winter Simulation Conference,2002:121-134.
[70]Ruiz-Torres A J, Lopez F J, Ho J C. Scheduling uniform parallel machines subject to a secondary resource to minimize the number of tardy jobs[J]. European Journal of Operational Research,2007,(179):302-315.
[71]Sabuncuoglu I., Bayiz M. Analysis of reactive scheduling problems in a Job shop environment[J]. European Journal of Operational Research,2000,126(3):567-586.
[72]Sadi A.A.Causes of delay in large construction project[J]. Journal of Project management,2006, (24):349-357.
[73]Seckiner. S.U., Gokcen. H., Kurt. M. An integer programming model for hierarchical workforce scheduling problem[J].European Journal of Operational Research. 2007.183(12):694-699.
[74]Sha. D.Y., Lin. H.H. A multi-objective PSO for job-shop scheduling problems[J]. Expert Systems with Applications.2010.37(3):1065-1070.
[75]Shafaei R., Brunn P. Workshop scheduling using practical(inaccurate)data-Part 2:Aninvestigation of the robustness of scheduling rules in a dynamic and stochastic environment[J]. International Journal of Production Research,1999,37(18),4105-4117.
[76]Sheen G J, Liao L W. Scheduling machine-dependent jobs to minimize lateness on machines with identical speed under availability constraints[J]. Computers & Operations Research,2007,(34):2266-2278.
[77]Simons Jr J.V., Simpson III W.P., An exposition of multiple constraint scheduling as implemented in the goal system (formerly DISASTERTM) [J]. Production and Operations Management,1997,6:3-22.
[78]Simons Jr J.V., Simpson III W.P., Carlson B.J., James S.W., Lettiere C.A., Mediate Jr B.A., Formulation and solution of the drum-buffer-rope constraint scheduling problem (DBRCSP) [J]. International Journal of Production Research,1996,34:2405-2420.
[79]Simons Jr J.V., Stephens M.D., Simpson III W.P., Simultaneous versus sequential scheduling of multiple resources which constrain system throughput[J]. International Journal of Production Research,1999,37:21-33.
[80]Spohrer.J, Maglio.P. Emergence of service science:services sciences, management, engineering (SSME) as the next frontier in innovation[EB/OL].(2005209208) [2006210210]. http://www.ibm.com/ibm/governmentalprogrames/SSME.pdf.
[81]Stolletz. R. Operational workforce planning for check-in counters at airports[J]. Transportation Research Part E:Logistics and Transportation Review.2010.46(5): 414-425.
[82]Sun J., Xue D. A dynamic reactive scheduling mechanism for responding to changes of production orders and manufacturing resources[J]. Computers in Industry,2001,46(2): 189-207.
[83]Tang H B, Ye C M. Application of plant growth simulation algorithm for job shop scheduling[C]. Advanced Management Science (ICAMS). Chengdu.2010:568-572.
[84]Uzsoy R., Wang C.S., Performance of decomposition procedures for Job shop scheduling problems with bottleneck machines[J]. International Journal of Production Research,2000,38:1271-1286.
[85]Valls. V., Perez. A., Quintanilla. S. Skilled workforce scheduling in Service Centres[J]. European Journal of Operational Research.2009.193(3):791-804.
[86]Vepsalainen A.P.J., Morton T.E., Priority rules for job shops with weighted tardiness costs[J]. Management Science,1987,33:1035-1047.
[87]Vieira G.E., Herrmann J.W., Lin E. Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies[J]. International Journal of Production Research,2000,38(8):1899-1915.
[88]Volgenant.A., Duin.C.W.. Improved polynomial algorithms for robust bottleneck problems with interval data[J]. Computers & Operations Research.2010.37(3).909-915.
[89]Wang CH, Cheng H ZH. Transmission network optimal planning based on plant growth simulation algorithm[J]. European Transactions on Electrical Power,2009,19 (2) 291-301.
[90]Yan. L., Song. B.J., Feng. H.X., Ye.J. A heuristic project scheduling approach for quick response to maritime disaster rescue[J]. International Journal of Project Management. 2009.27(8):620-628.
[91]Yazdani. M., Amiri. M., Zandieh. M. Flexible job-shop scheduling with parallel variable neighborhood search algorithm[J]. Expert Systems with Applications.2010.37(1):678-687.
[92]Zhang. F, Cao. X, Yang. D. Intelligent Scheduling of Public Traffic Vehicles Based on a Hybrid Genetic Algorithm[J] Tsinghua Science & Technology.2008.13(5):625-631.
[93]Zhang. G, Shao. X, Li. P., Gao. L. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J]. Computers & Industrial Engineering,2009.56(5):1309-1318.
[94]Zuo.Y, G.H, Xi.. Modified bottleneck-based heuristic for large-scale job-shop scheduling problems with a bottleneck[J]. Journal of Systems Engineering and Electronics.2007.18:556-565.
[95]陈旭梅,林国鑫,于雷.常规公共交通与轨道交通运营调度协调模型[J].系统工程理论与实践.2009.29(10)：165-173.
[96]董红宇,黄敏,王兴伟,郑秉霖.一体化炼钢炉次计划模型与算法[J].系统仿真学报.2009.21(8)：4801-4805.
[97]郭重庆.服务科学_一个极具前沿意义的学科[J].中国科学基金.2008.(4)：217-220.
[98]郭永辉Drum-Buffer-Rope理论研究综述[J].工业工程与管理.2008.(2)：1-4.
[99]李敬花.遗传蚁群融合算法求解多项目资源能力平衡问题[J].计算机集成制造系统.2010.12(2)：183-187.
[100]李彤,王春峰,王文波,宿伟玲.求解整数规划的一种仿生类全局优化算法——模拟植物生长算法[J].系统工程理论与实践,2005.1(1)：76-85.
[101]李彤,王众托.模拟植物生长算法与知识创新的几点思考[J].管理科学学报,2010,13(3)：87-96.
[102]李彤,王众托.模拟植物生长算法在设施选址问题中的应用[J].系统工程理论与实践,2008,28(]2)：107-115.
[103]李妍峰,李军,赵达.基于迭代局域搜索的智能优化算法求解车辆调度问题研究[J].系统工程理论与实践,2007(5)：75-81
[104]刘民,吴澄,尹文龙.带特殊工艺约束的并行机器生产线调度问题的一种遗传算法[J].自动化学报,2001,27(3)：381-386.
[105]刘士新.项目优化调度理论与方法[M].北京：机械工业出版社,2007：1-42.
[106]刘志雄,王少梅.基于粒子群算法的并行多机调度问题研究[J].计算机集成制造系统,2006,12(2)：183-187
[107]刘作仪,杜少甫.服务科学管理与工程：一个正在兴起的领域[J].管理学报,2008,5(7)：607-614
[108]苗东升.系统科学精要[M].北京：中国人民大学出版社,1998：1-67.
[109]彭武良,王成恩.关键链项目调度模型及遗传算法求解[J].系统工程学报,2010,25(2)：123-131.
[110]涂序彦,王枞,郭燕慧.大系统控制论[M].北京：北京邮电大学出版社,2005：1-58.
[111]王东生,曹磊.混沌、分形及其应用[M].合肥：中国科学技术大学出版社,1995：1-93.
[112]王凌.车间调度及其遗传算法[M].北京：清华大学出版社,2003：1-102.
[113]王谦,陈芳莲.多系统服务中心的多技能服务人员调度问题研究[J].系统科学与数学,2008,28(11)：1337-1345.
[114]王玉荣.瓶颈管理[M].北京：机械工业出版社,2002：1-149.
[115]吴斌,蔡红,樊树海,蒋南云.双倍体差分进化粒子群算法在VRPSDP中的应用研 究[J].系统工程理论与实践,2010,30(3)：520-526.
[116]席裕庚.动态大系统方法导论[M].北京：国防工业出版社,1988：1-72.
[117]肖人彬,陶振武.群集智能研究进展[J].管理科学学报,2007,10(6)：80-91.
[118]张龙,刘民,刘涛,吴澄.复杂产品开发项目调度问题的模糊优化算法[J].控制工程,2009,16(11)：731-734.
[119]左燕,谷寒雨,席裕庚.大规模流水线调度的瓶颈分解算法研究[J].控制与决策,2006,21(4)：425-429.