作业车间基于漂移瓶颈的物料流控制方法研究
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摘要
随着经济和生产力的发展，市场需求日趋多样化和个性化，使得生产模式由传统的单一品种（或少品种）大批量逐渐转变为多品种小批量，生产作业车间内存在的各种不确定性因素对物料流影响随之增大，给车间内物料流控制带了严峻的挑战。推拉结合的TOC控制模式虽然能够通过对关键瓶颈的控制，提高物料流与生产节奏的同步性；然而，传统的TOC控制模式无法适用于由于不确定性因素而导致车间瓶颈频繁漂移的生产环境。为此，本文提出并研究了适应于不确定环境下存在瓶颈漂移现象的作业车间物料流控制方法。
首先，对目前物料流控制相关邻域的研究现状进行了综述，分析了现代制造模式下作业车间环境的特点和问题，指出了现代制造环境下物料流控制方式与传统TOC控制模式的区别，将识别瓶颈和预测瓶颈漂移趋势作为物料流控制的前提。
接着，围绕着不确定环境下物料流控制的核心问题，从漂移瓶颈辨识、物料投放、物料流路径以及物料流稳定性等方面进行了深入研究，具体包括：
1)在明确了作业车间物料流内涵以及物料流瓶颈漂移定义的基础上，提出了导致物料流瓶颈漂移的作业车间不确定性因素变量的数字化描述方法；利用马尔科夫链、神经网络算法分析了不确定因素间耦合关系；建立物料流瓶颈综合指数体系包括瓶颈指数、瓶颈漂移指数和瓶颈敏感指数，以实现对不确定环境下作业车间内漂移瓶颈的识别和预测。
2)根据作业车间内各制造单元的瓶颈状态、漂移特点以及主次瓶颈个数，提出了基于漂移瓶颈物料投放控制方法，包括对初始物料的准入控制以及制造单元间生产(转运)批量和提前期的控制；针对物料准入控制，提出了作业车间内瓶颈单元在制品上限、瓶颈间在制品上限和瓶颈上游单元在制品目标值作为准入依据；针对制造单元间的期量控制，采用物料流瓶颈综合指数表征制造单元内在制品流动节奏，建立以最小化非增值加工时间和相邻制造单元物料流瓶颈程度差异为优化目标的制造单元间期量设定模型，以保证各制造单元间物料流节奏的同步性，并利用粒子群算法求解该模型。
3)在计及漂移瓶颈对物料配送路径选择影响的基础上，采用瓶颈指数和瓶颈漂移指数表征实时变化的制造单元物料配送优先级，对路径选择过程中违反此优先级的行为设置惩罚成本，提出以最小化包括车辆运输成本和违反优先级的惩罚成本在内的总配送成本为优化目标，建立时变的物料配送路径优化模型；为保证运输车辆所载物料全额配送，避免非必要负载以及由此造成的非必要配送子路径，令该模型允许运输车辆非满载和物料拆分配送，以提高物料配送效率降低配送成本；并结合模型特点将贪婪策略融入遗传算法对优化模型求解。
4)对物料流鲁棒性和稳定性内涵进行了区分，以单机车间为特例建立不确定加工时间环境下的物料流稳定性控制模型并提出两种启发式算法对该NP-hard问题进行求解；在此基础上，将单机问题扩展至多制造单元作业车间，提出了两种多制造单元物料流稳定性控制策略(瓶颈单元控制策略和瓶颈上游单元控制策略)，并通过实例比较和分析了两种策略的适用范围，表明运用该稳定性控制策略能够抑制作业车间物料流瓶颈的漂移、降低物料流控制的难度。
With the development of economy and productivity, the market and customer demand becomesdiversification and individuation gradually, which makes the production mode change fromsingle-item big-lot to multi-item small-lot. The influence of various uncertain factors in job shop onthe material flow increases as the production mode changes, which leads to severe challenges for thecontrol of material flow. Although TOC control mode can enhance the synchronism betweenmaterial flow and production pace by controlling the key bottleneck, traditional TOC mode can notadapt the uncertain production environment with bottleneck shifting. Therefore, this dissertationproposes and researches the control method of the material flow under uncertainty with thephenomenon of the shifting bottleneck.
Firstly, the literature review of the related research field about material flow control issurveyed. The characteristics of production environment under modern production mode is analyzed,then the difference between the material flow control method under modern production mode andtraditional TOC control method is indicated. It shows that the identification and prediction of theshifting bottleneck is the premise of the material flow control.
Then, focusing on the core problem of matrial flow control under uncertainty, the identificationand prediction of the shifting bottleneck, the matrial release, the route of matrial flow and thestability of the material flow are studied. The content is as follows:
1) Based on the concept of the material flow and the definition of the shifting bottleneck in jobshop, the digitalization method of the uncertain factor which cause the bottleneck shift is proposed.Using Markov chain and neural network to analyze the coupling relationship among the uncertainfactors, then establish the integrated bottleneck measurement system including bottleneck index,bottleneck shifting index and the bottleneck sensitivity index, in order to identify and prodict theshifting bottleneck in job shop under uncertainty.
2) According to the bottleneck status of each manufacturing unit, bottleneck shifting trend andthe number of the bottlenecks, a control method of material release based on the shifting bottleneckis proposed, which includes the control of the raw material input and the control of the transfer lotand lead time. Aming at the control of raw material input, WIP upbound of the bottleneck unit, theWIP upbound between bottleneck units and the WIP target value of upstream unit is presented as thecriterion of the material input. Aming at the control of the transfer lot and lead time, adopting thebottleneck index and the bottleneck shifting index to represent the pace of WIP flow, establish thesetting model of the transfer lot and lead time of which the objectives are minimizing thenon-value-added processing time and minimizing the difference of the bottleneck degree betweenthe adjacent manufacturing units, and use the particle swarm heuristic to solve this model.
3) Based on considering the influence of the shifting bottleneck on the optimization of materialdistribution route, use the bottleneck index and the bottleneck shifting index to indicate the priorityof material distribution of each manufacturing unit, and provide the punishment cost if violate thepriority during the distribution route optimization. Hence, the objective of the established model oftime-varing material distribution route optimization is minimizing the total distribution costincluding the transportation cost and the punishment costdue to the violation of distribution priority.In order to guarantee that the material in the vehicle is distributed totally before the vehicle comesback to the distribution center and to avoid unnecessary distribution sub-route, suppose the condition that the vehicle haven’t to be loaded fully and material can be distributed to one manufacturing moretimes, which can enhance the distribution productivity and decrease the distribution cost further.Then, a greedy-based genetic algorithm is presented to solve the proposed optimization model.
4) On the basis of differentiating the concept of the stability and roubustness of the materialflow, take a single machine shop as a special case to build the staility control model of the matrialflow under uncertain processing time and propose two heuristics to solve this NP-hade problem.Then generate the model and the result from a special case (single machine job shop) to the generalcase (multi-machine job shop) and present two control policy for multi-machine job shop—thecontrol policy of the bottleneck unit and the control policy of the upstream unit of the bottleneck.Further, according to the case study, analyze and compare the adaptation condition of two controlpolicies and the results shows that the two control policies can help to hedge the bottleneck shift andreduce the difficulty of the matrial flow control.
首先，对目前物料流控制相关邻域的研究现状进行了综述，分析了现代制造模式下作业车间环境的特点和问题，指出了现代制造环境下物料流控制方式与传统TOC控制模式的区别，将识别瓶颈和预测瓶颈漂移趋势作为物料流控制的前提。
接着，围绕着不确定环境下物料流控制的核心问题，从漂移瓶颈辨识、物料投放、物料流路径以及物料流稳定性等方面进行了深入研究，具体包括：
1)在明确了作业车间物料流内涵以及物料流瓶颈漂移定义的基础上，提出了导致物料流瓶颈漂移的作业车间不确定性因素变量的数字化描述方法；利用马尔科夫链、神经网络算法分析了不确定因素间耦合关系；建立物料流瓶颈综合指数体系包括瓶颈指数、瓶颈漂移指数和瓶颈敏感指数，以实现对不确定环境下作业车间内漂移瓶颈的识别和预测。
2)根据作业车间内各制造单元的瓶颈状态、漂移特点以及主次瓶颈个数，提出了基于漂移瓶颈物料投放控制方法，包括对初始物料的准入控制以及制造单元间生产(转运)批量和提前期的控制；针对物料准入控制，提出了作业车间内瓶颈单元在制品上限、瓶颈间在制品上限和瓶颈上游单元在制品目标值作为准入依据；针对制造单元间的期量控制，采用物料流瓶颈综合指数表征制造单元内在制品流动节奏，建立以最小化非增值加工时间和相邻制造单元物料流瓶颈程度差异为优化目标的制造单元间期量设定模型，以保证各制造单元间物料流节奏的同步性，并利用粒子群算法求解该模型。
3)在计及漂移瓶颈对物料配送路径选择影响的基础上，采用瓶颈指数和瓶颈漂移指数表征实时变化的制造单元物料配送优先级，对路径选择过程中违反此优先级的行为设置惩罚成本，提出以最小化包括车辆运输成本和违反优先级的惩罚成本在内的总配送成本为优化目标，建立时变的物料配送路径优化模型；为保证运输车辆所载物料全额配送，避免非必要负载以及由此造成的非必要配送子路径，令该模型允许运输车辆非满载和物料拆分配送，以提高物料配送效率降低配送成本；并结合模型特点将贪婪策略融入遗传算法对优化模型求解。
4)对物料流鲁棒性和稳定性内涵进行了区分，以单机车间为特例建立不确定加工时间环境下的物料流稳定性控制模型并提出两种启发式算法对该NP-hard问题进行求解；在此基础上，将单机问题扩展至多制造单元作业车间，提出了两种多制造单元物料流稳定性控制策略(瓶颈单元控制策略和瓶颈上游单元控制策略)，并通过实例比较和分析了两种策略的适用范围，表明运用该稳定性控制策略能够抑制作业车间物料流瓶颈的漂移、降低物料流控制的难度。
With the development of economy and productivity, the market and customer demand becomesdiversification and individuation gradually, which makes the production mode change fromsingle-item big-lot to multi-item small-lot. The influence of various uncertain factors in job shop onthe material flow increases as the production mode changes, which leads to severe challenges for thecontrol of material flow. Although TOC control mode can enhance the synchronism betweenmaterial flow and production pace by controlling the key bottleneck, traditional TOC mode can notadapt the uncertain production environment with bottleneck shifting. Therefore, this dissertationproposes and researches the control method of the material flow under uncertainty with thephenomenon of the shifting bottleneck.
Firstly, the literature review of the related research field about material flow control issurveyed. The characteristics of production environment under modern production mode is analyzed,then the difference between the material flow control method under modern production mode andtraditional TOC control method is indicated. It shows that the identification and prediction of theshifting bottleneck is the premise of the material flow control.
Then, focusing on the core problem of matrial flow control under uncertainty, the identificationand prediction of the shifting bottleneck, the matrial release, the route of matrial flow and thestability of the material flow are studied. The content is as follows:
1) Based on the concept of the material flow and the definition of the shifting bottleneck in jobshop, the digitalization method of the uncertain factor which cause the bottleneck shift is proposed.Using Markov chain and neural network to analyze the coupling relationship among the uncertainfactors, then establish the integrated bottleneck measurement system including bottleneck index,bottleneck shifting index and the bottleneck sensitivity index, in order to identify and prodict theshifting bottleneck in job shop under uncertainty.
2) According to the bottleneck status of each manufacturing unit, bottleneck shifting trend andthe number of the bottlenecks, a control method of material release based on the shifting bottleneckis proposed, which includes the control of the raw material input and the control of the transfer lotand lead time. Aming at the control of raw material input, WIP upbound of the bottleneck unit, theWIP upbound between bottleneck units and the WIP target value of upstream unit is presented as thecriterion of the material input. Aming at the control of the transfer lot and lead time, adopting thebottleneck index and the bottleneck shifting index to represent the pace of WIP flow, establish thesetting model of the transfer lot and lead time of which the objectives are minimizing thenon-value-added processing time and minimizing the difference of the bottleneck degree betweenthe adjacent manufacturing units, and use the particle swarm heuristic to solve this model.
3) Based on considering the influence of the shifting bottleneck on the optimization of materialdistribution route, use the bottleneck index and the bottleneck shifting index to indicate the priorityof material distribution of each manufacturing unit, and provide the punishment cost if violate thepriority during the distribution route optimization. Hence, the objective of the established model oftime-varing material distribution route optimization is minimizing the total distribution costincluding the transportation cost and the punishment costdue to the violation of distribution priority.In order to guarantee that the material in the vehicle is distributed totally before the vehicle comesback to the distribution center and to avoid unnecessary distribution sub-route, suppose the condition that the vehicle haven’t to be loaded fully and material can be distributed to one manufacturing moretimes, which can enhance the distribution productivity and decrease the distribution cost further.Then, a greedy-based genetic algorithm is presented to solve the proposed optimization model.
4) On the basis of differentiating the concept of the stability and roubustness of the materialflow, take a single machine shop as a special case to build the staility control model of the matrialflow under uncertain processing time and propose two heuristics to solve this NP-hade problem.Then generate the model and the result from a special case (single machine job shop) to the generalcase (multi-machine job shop) and present two control policy for multi-machine job shop—thecontrol policy of the bottleneck unit and the control policy of the upstream unit of the bottleneck.Further, according to the case study, analyze and compare the adaptation condition of two controlpolicies and the results shows that the two control policies can help to hedge the bottleneck shift andreduce the difficulty of the matrial flow control.
引文
[1]李培根.制造系统性能分析建模——理论与方法[M].武汉：中华理工大学出版社,1985.
[2]王之泰.现代物流管理[M].北京：中国工人出版社,2001.
[3] Hopp W J, Spearman M L. Factory physics[M]. Irwin McGraw-Hill,2nd edition,2000.
[4] Ritzman L P, Krajewski L J. Comparison of material requirements planning and reorder pointsystem[J]. Simulation Councils Proceedings Series,1984,12(2):47-54.
[5] Amin, M, Altiok T. Control policies for multi-product multi-stage manufacturing systems: anex-perimental approach[J]. International Journal of Production Research,1997,35(1):201-223.
[6] Zijm W H M, Buitenhek R. Capacity planning and lead time management[J]. InternationalJournal of Production Economics,1996,46-47:165-179.
[7] Gahagan S M, Herrmann J W. Finding the optimal production control policy using theproduction control framework [C]. Proceedings of the2005Winter Simulation Conference,2005:1418-1427.
[8]王辉.雪铁龙公司拉动式物流基本概念[J].物流技术,1996,4.
[9] Spearman M L, Wookdruff D L, Hopp W J. CONWIP: a pull alternative to kanban[J].International Journal of Production Research,1990,28(5):879-894.
[10] Bonvik A M, Couch C, Gershwin S B. A comparison of production-line control mechanisms[J].International Journal of Production Research,1997,35(3):789-804.
[11] Spearman M L, Zazanis M A. Push and pull production systems: issue and comparison[J].Operations Research,1992,40(3):521-532.
[12] A. Agnetis, A. Pacifici, F. Rossi, M. Lucertini, S. Nicoletti, F. Nicolò, G. Oriolo, D. Pacciarelli,E. Pesaro. Scheduling of flexible flow lines in an automobile assembly plant[J], European Journalof Operational Research,1997,97(2):348-362.
[13] Sader B H. Development and application of a new modeling technique for production controlschemes in manufacturing systems[D]. Brigham Young University,2005.
[14] Spearman M L. Customer service in pull production system[J]. Operations Research,1992,40(5):948-958.
[15] Bonvik A M, Dallery Y, Gershwin S B. Approximate analysis of production systems operatedby a conwip/finite buffer hybrid control policy[J]. International Journal of Production Research,2000,38(13):2845-2869.
[16] Goldratt E. The goal: a process of ongoing improvement[M]. Great Barrington, MA: NorthRiver Press Inc,1984.
[17] Cook D P. A simulation comparison of traditional, JIT, and TOC manufacturing systems in aflow shop with bottlenecks[J]. Production and Inventory Management,1994,35:73-78.
[18] Guide V D R. Scheduling using drum-buffer-rope in a remanufacturing environment[J].International Journal of Production Research,1996,34(4):1081-1091.
[19] Altug M S. Design and performance evaluation of mixed MRP, Kanban, and CONWIPproduction systems using emulated flexible manufacturing laboratory[D]. University of Florida,Gainesville,1997.
[20] Blake N, Hellberg R. Relevance lost? A critical discussion of different cost accountingprinciples in connection with decision making for both short and long term productionscheduling[J]. International Journal of Production Economics,1991,24:1-18.
[21] Srivatsan N.. Synthesis of optimal policies for stochastic manufacturing systems[D].Massachusetts Institute of Technology,1993.
[22] Gershwin, S.B. System analysis, design and control: Unification and decomposition[C],Second Aegean International Conference on Modeling of Manufacturing Systems, Tinos Island,Greece,1999.
[23] Gershwin S B. Design and operation of manufacturing systems-the control point policy[J].IIE Transactions,2000,32(2):891-906.
[24] Deshpande S P. A scheduling policy experiment for lean implementation[D]. MassachusettsInstitute of Technology,1999.
[25] Gzouli O. Comparison of scheduling policies by simulation[D]. Massachusetts Institute ofTechnology,2000.
[26] Yong M S. Simulation of real-time scheduling policies in multi-product, make-to-ordersemiconductor facilities[D]. Massachusetts Institute of Technology,2001.
[27] Kim C. Comparison and optimization of control policies in automobile manufacturing systemsby simulation[D]. Massachusetts Institute of Technology,2004.
[28] Jason Ardon-Finch B A, Riham K. Control point policy: part1-efficiency withinmake-to-order environments[J]. International Journal of Production Research,2008,46(11):2927-2943.
[29] Breithaupty J W. Controlling production dynamics-managing uncertainties with automaticproduction control[J]. International Journal of Production Research,2000,38(13):4235-4246.
[30] Christoph Roser, Masaru Nakano, Minoru Tanaka. A Practical Bottleneck DetectionMethod[A]. Proceedings of the2001Winter Simulation Conference, Arlington,2001:949-953.
[31] Christoph Roser, Masaru Nakano, Minoru Tanaka. Shifting Bottleneck Detection[C].Proceedings of the2002Winter Simulation Conference, San Diego,2002:1079-1086.
[32] Christoph Roser, Masaru Nakano, Minoru Tanaka.Comparison of Bottleneck DetectionMethods for AGV Systems [A]. Proceedings of the2003Winter Simulation Conference, NewOrleans,2003:1192-1198.
[33] Patrick Faget, Ulf Eriksson, Frank Herrmana. Appling Discrete Event Simulation and anAtuomated Bottleneck Analysis as an Aid to Detect Running Production Constraints[C].Proceedings of the2005Winter Simulation Conference,2005:1401-1407.
[34] ShiNung Ching, Semyon M Meerkov, Liang Zhang.Assembly systems with non-exponentialmachines: Throughput and bottlenecks [J]. Nonlinear Analysis: Theory, Methods&Applications,2008,69(31):911-917.
[35] Li L, Chang Q, Ni J. Data driven bottleneck detection of manufacturing systems [J].International Journal Of Production Research,2009,47(18):5019-5036.
[36] Chang Q, Ni J, Bandyopadhyay P, Biller S, Xiao G. Supervisory Factory Control Based onReal-Time Production Feedback[J]. Journal of Manufacturing Science and Engineering,2007,6(129):653-660.
[37] Pegels C.Carl, Watrous Craig. Application of the theory of constraints to a bottleneckoperation in a manufacturing plant [J]. Journal of Manufacturing Technology Management,2005,3(16):302-311.
[38] Muthiah KMN, Huang SH. Overall throughput effectiveness(OTE) metric for factory levelperformance monitoring and bottleneck detection[J]. Internatioal Journal of Production Research,2007,45(20):4753-4769.
[39] Muthiah KMN, Huang SH, Sangeetha Mahadevan. Automating factory performancediagnostics using overall throughput effectiveness (OTE) metric[J]. The International Journal ofAdvanced Manufacturing Technology,2008,36:811-824.
[40] Ingemansson A, Torbjo¨rn Ylipa¨a¨, Gunnar S.Bolmsjo. Reducing bottlenecks in amanufacturing data collection and discrete-event simulation[J]. Journal of ManufacturingTechnology Management,2005,16(6):615-628.
[41] Babu T R, Rao K. S. P., Maheshwaran C. U. Application of TOC embedded ILP for increasingthroughput of Production lines [J]. International Journal of Manufacturing Technology andManagement,2007,33(7-8):812-818.
[42] Richard A R. Applying the TOC five-step focusing process in the service sector-A bankingsubsystem [J].Managing Service Quality,2007,17(02):209-234.
[43] Miyashita K, Rajesh G. Multiagent coordination for controlling complex and unstablemanufacturing processes[J]. Expert Systems with Applications,2010,37(03):1836-1845.
[44] Choi S H, Yang F Y. A filter search algorithm based on machine-order space for job-shopscheduling problems [J]. Journal of Manufacturing Technology Management,2006,3(17):376-392.
[45] Chen Chun-Lung, Chen Chuen-Lung. A Heuristic Method for a Flexible Flow Line withUnrelated Parallel Machines Problem[A]. Robotics,Automation and Mechatronics,2006IEEEConference, Chicago,2006:1-4.
[46]高文会.约束理论的瓶颈识别研究[J].西安石油大学学报(社会科学版),2008,(01):51-56.
[47] Sawik T. Multi-objective due-date setting in a make-to-order environment[J]. InternationalJournal Of Production Research,2009,47(22):6205-6213.
[48] Rajakumar S, Arunachalam V P, Selladurai V. Workflow balancing in parallel machinescheduling with precedence constraints using genetic algorithm[J]. Journal of ManufacturingTechnology Management,2006,17(02):239-254.
[49] Zhang R, Wu C. Bottleneck identification procedures for the job shop scheduling problem withapplications to genetic algorithms[J]. The International Journal of Advanced ManufacturingTechnology,2009,6(42):1153-1164.
[50]李晓红,周炳海.晶圆制造过程中动态瓶颈设备的实时调度[J].上海交通大学学报,2008,(04):599-602.
[51]乔非,许潇红,方明,吴启迪.半导体晶圆生产线调度的性能指标体系研究[J].同济大学学报(自然科学版),2007,(04):537-542.
[52]徐云晴，邵炜，李树刚.基于SAP和TOC的瓶颈识别[J].苏州大学学报(自然科学版),2008,24(04):63-68.
[53]陈菊红,史成东,杨阳.供应链产品物流过程模型及时间瓶颈的识别[J].中国管理科学,2009,17(06):63-69.
[54] Wang S X. A look-ahead heuristic algorithm for dynamic identify bottleneck machine[J].Industrial Engineering Journal,2008,7(11):127-131.
[55]郭永辉.基于产出速度的Job-shop型企业生产作业计划与控制方法[J].工业工程,2008,11(05):86-89.
[56]周夫利，韩文民，刘志勇，龚俏巧.基于约束理论的车间调度仿真系统研究与设计[J].自动化仪表,2009,30(05):11-13.
[57]许一敏.基于瓶颈的BTO供应链响应时间优化与控制研究[D].华中科技大学,2009.
[58]邹律龙,侯东亮.面向订单的瓶颈资源识别与单机成组作业调度[J].现代机械,2009,(02):91-93.
[59] Carlos D P, Jairo R M T, Milton J A D, Maria C H H. Scheduling jobs on a k-stage flexibleflow-shop[J]. Annals of Operations Research,2008,11(164):29-40.
[60] Milton J A-D, Carlos D P-A. Scheduling Jobs on a K_Stage Flexible Flow Shop Using aTOC-Based (Bottleneck) Procedure: Proceeding of the2004Systems and Information EngineeringDesign Symposium[C].2004:295-298.
[61]沈妙妙,陈雷雷,鲁建厦.生产瓶颈转移性影响因素的经济计量研究[J].上海电机学院学报,2008,(02):139-143.
[62]王国磊，林琳，钟诗胜.采用可用度理论的瓶颈设备前缓冲时间估计[J].哈尔滨工业大学学报,2009,41(11):53-58.
[63]杨丽艳，钱省三.半导体晶圆生产系统瓶颈识别与管理对策[J].现代管理,2008,32(12):1021-1024.
[64]张怀，江志斌，郭乘涛.面向瓶颈的半导体晶圆制造系统派工策略及参数优化[J].上海交通大学学报，2007,41(08):1252-1257.
[65] Liao C J. Optimal release time on a stochastic single-machine[J]. International Journal ofSystems Science,1992,23(10):1693-1701.
[66] Gong L G, Kok T, Ding J. Optimal lead-times planning in a serial production system[J].Management Science,1994,40(5):629-632.
[67] Buss A H, Rosenblatt M J. Activity delay in stochastic project networks[J]. OperationResearch,1997,45(1):126-139.
[68] Elmaghraby S E, Ferreira A A, Tavares L V. Optimal start times under stochasitc activitydurations[J]. International Journal of Production Economics,2000,64:153-164.
[69] Elmaghraby S E. On the optimal release time of jobs with random processing times withextensions to other criteria[J]. International Journal of Production Economics,2001,74:103-113.
[70] HomemdeMell T, Shapiro A, Spearman M L. Finding optimal material release times usingsimulation-based optimisation[J]. Management Science,1999,45(1):86-102
[71] Hasan C N, Spearman M L. Optimal material release times in stochastic productionenvironments[J]. International Journal of Production Research,1999,37(6):1201-1216.
[72] Song D P, Hicks C, Earl C F. Setting planned job release times in stochastic assembly systemswith resource constraints[J]. International Journal of Production Research,2001,39(6):1289-1301.
[73] Song D P. Raw material release time control for complex make-to-orderproducts withstochastic processing times[J]. International Journal of Production Economics,2006,103:371–385.
[74] Nahavandi N. CWIPL II, a mechanism for improving throughput and lead time in unbalancedflow line[J]. International Journal of Production Research,2009,47(11):2921-2941.
[75]崔南方,刘英姿,严仕锋.物料需求计划提前期设置的改进方法[J].华中科技大学学报(自然科学版),2003,31(5):95-97.
[76] Missbauer R. Models of the transient behaviour of production units to optimizethe aggregatematerial flow[J]. Int. J. Production Economics,2009,118:387–397.
[77] Le Q S, Knapp G M. Incorporating fuzzy logic admission control in simulation models[C].Winter Simulation Conference Proceedings,2003,2:1276-1280.
[78] Cheng C B, Liu Z Y, Chiu S W, Cheng C J. Applying fuzzy logic control to inventory decisionin a supply chain[J]. WSEAS Transactions on System,2006,5(8):1822-1829.
[79] Sarker B R, Parija G R. Optimal batch size and raw material ordering policy for a productionsystem with a fixed-interval lumpy demand delivery system[J]. European Journal of OperationalResearch,1996,89:593-608.
[80] Sarker R A, Khana L R. An optimal batch size for a production system periodic deliverypolicy[J]. Computers&Industrial Engineering,1999,37:711-730.
[81] Khana L R, Sarker R A. An optimal batch size for a JIT manufacturing system[J]. Computers&Industrial Engineering,2002,42:127-136.
[82] Grubbstrom R W, Bogataj M, Bogataj L. Optimal lotsizing within MRP Theory[J]. AnnualReviews in Control.2010,34:89–100.
[83] Biller S, Meerkov S M, Yan C B. Raw material release rates to ensure desired production leadtime in Bernoulli serial lines[J]. International Journal of Production Research,2013,51:14,4349-4364.
[84]郝刚,吴功宜.根据日产量动态调整批量和提前期的物料需求计划研究[J].计算机集成制造系统,2005,11(9):1267-1271.
[85]武保全,孙棣华.基于泛在制造信息的动态物料配送批量决策[J].现代制造工程,2013,1:16-19+37.
[86] Rosenblatt M J. Dynamics of plant layout [J]. Management Science,1986,32(1):76–86.
[87] Rosenblatt M J, Lee H L. A robustness approach to facilities design[J]. International Journal ofProduction Research,1987,25:479–486.
[88] Balakrishnan J, Jacobs F R, Venkataramanan M A. Solutions for the constrained dynamicfacility-layout problem[J]. European Journal of Operational Research,1992,57:280–286.
[89] Palekar U S, Batta R, Bosch R M, et al. Modeling uncertainties in plant layout problems[J].European Journal of Operational Research,1992,63:347–359.
[90] Urabm T L. Computational performance and efficiency of lower-bound procedures for thedynamic facility layout problem[J]. European Journal of Operational Research,1992,57:271–279.
[91] Urabm T L. A heuristic for the dynamic facility layout problem[J]. IIE Transactions,1993,25(4):57–63.
[92] Urabm T L. Solution procedures for the dynamic facility layout problem[J]. Annals ofOperations Research,1998,76:323–342.
[93] Kouveils P, Kiran A S. Single and multiple period layout models for automated manufacturingsystems[J]. European Journal of Operational Research,1991,52:300–314.
[94] Kouvelis, P., Kurawarwala, A. A., and Gutierrez, G. J. Algorithms for robustsingle andmultiple period layout planning for manufacturing systems[J]. European Journalof OperationalResearch,1992,63:287-303.
[95] Yang T. and Peters B A. Flexible machine layout design for dynamic and uncertain productionenvironments[J]. European Journal of Operational Research,1998,108:49–64.
[96] Kochhar J S, Heragu S S. Facility layout design in a changing environment[J]. InternationalJournal of Production Research,1999,37:2429–2446.
[97] Pillaia V. M, Hunagunda I B, Krishnan K K. Design of robust layout for Dynamic PlantLayout Problems[J]. Computers&Industrial Engineering,2011,61:813–823.
[98] Lee T S, Moslemipour G. Intelligent design of a flexible cell layout with maximum stability ina stochastic dynamic situation[C].//1st International Conference on Intelligent Robotics,Automation and Manufacturing, IRAM2012, Kuala Lumpur, Malaysia, November28-November30,2012:398-405.
[99] Curry G L, Peters B A, Lee M. Queueing network model for a class of material-handlingsystems[J]. InternationalJournal of Production Research,2003,41(16):3901–3920.
[100] Sukhotu V. Material handling system models and block layout approaches for facilitydesign[D]. Texas A&M University,2002.
[101] Sukhotu V, Peters B A. Modelling of material handling systems for facilitydesign inmanufacturing environments with job-specific routing[J]. International Journal of ProductionResearch,2012,24(15):7285–7302.
[102] Fisher M. Vehicle routing[M]. In: Handbooks in Operations Research and ManagementScience, North-Holland, Amsterdam,1995,8:1-33.
[103] Renaud J, Laporte G, Boctor F F. A tabu search heuristic for the multi-depot vehicle routingproblem[J]. Computers and Operations Research,1996,23(3):229-235.
[104] Baker B M, Ayechew M A. A genetic algorithm for the vehicle routing problem[J].Computers&Operations Research,2003,30:787-800.
[105]郎茂祥.用单亲遗传算法求解配送车辆调度问题的研究[J].交通与计算机,2006,(01):119-121.
[106] Moghaddam R T, Safaei N, Gholipour Y. A hybrid simulated annealing for capacitatedvehicle routing problems with the independent route length[J]. Applied Mathematics andComputation,2006,176:445-454.
[107] Osman I H. Metastrategy simulated annealing and tabu search algorithms for the vehiclerouting problem[J]. Annual of Operations Research,1993,(41):421—451.
[108] Archetti C, Speranza M G, Savelsbergh M W P. An optimization-based heuristic for the splitdelivery vehicle routing problem[J]. Transportation Science,2008,42(1):22-31.
[109] Potvin J Y, Kervahut T, Garcia B L, et al. The vehicle routing problem with time windowspartⅠ: Tabu Search[J]. Informs Journal on Computing,1996,8(2):158-164.
[110] Favaretto D, Morette E, Pellegrini P. Ant colony system for a VRP with multiple timewindows and multiple visits[J]. Journal of Interdisciplinary Mathematics,2007,10(2):263-284.
[111] Donati A V, Montemanni R, Casagrande N, et al. Time dependent vehicle routing problemwith a multi ant colony system[J]. European Journal of Operational Research,2008,185:1174-1191.
[112] Yannis M, Magdalene M. A hybrid genetic-particle swarm optimization algorithm for Tehranvehicle routing problem[J]. Expert Systems with Application,2010,37:1446-1455.
[113]张丽艳,庞小红,夏蔚军,等.带时间窗车辆路径问题的混合粒子群算法[J].上海交通大学学报,2006,40(11):1890-1894+1900.
[114] Tan K C, Chew Y H, Lee L H. A hybrid multi-objective evolutionary algorithm for solvingvehicle routing problem with time windows[J]. Computational Optimization and Applications,2006,34:115-151.
[115] Russell B, Pascal V H. A two stage hybrid algorithm for pickup and delivery vehicle routingproblems with time windows[J]. Computers and Operations Research,2006,33:875-893.
[116]赵若彤.一种物流配送车辆路径智能优化算法研究[J].计算机与数字工程,2013,41(4):529-531+537.
[117]李敏,郭强,刘红丽.多车场多配送中心的物流配送问题研究[J].计算机工程与应用,2007,43(8):202-204+208.
[118] Hadjar A, Marcotte O, Soumis F. A branch-and-cut algorithm for the multiple depot vehiclescheduling problem[J]. Operations Research,2006,54(1):130-149.
[119] Dondo R,Cerda J. A cluster-based optimization approach for the multi-depot heterogeneousfleet vehicle routing problem with time windows[J]. European Journal of Operational Research,2007,176:1478-1507.
[120]于滨,靳鹏欢,杨忠振.两阶段启发式算法求解带时间窗的多中心车辆路径问题[J].系统工程理论与实践,2012,32(8):1793-1800.
[121] Tedorovid D, Pavkovic G. The fuzzy set theoryapproach to the vehicle routing problem whendemand atnodes is uncertain[J]. Fuzzy Sets and Systems，1996，82(3)：307-317.
[122]廖杨喆,王烨东,王一晗.基于专家系统的单天车配料实时路径优化与管理[J].价值工程,2010,32:200-201.
[123]周长峰,谭跃进,廖良才.实时条件下多车辆路径与调度[J].系统工程，2006,24(5):36-41.
[124]樊建华,王秀峰.随机需求多车辆路径问题的重优化算法[J].南开大学学报(自然科学版)，2008,41(2):103-107.
[125]李军.有时间窗的车辆路线安排问题的启发式算法.系统工程,1996,114(5):45-50。
[126] Taillard E, Badeau P, Gendreau M, et al. A Thbu Seareh Heuristie for the Vehiele RoutingProblem with Soft time Windows[J]. Transportation Scienee,1997,31(2):170-186.
[127] Gendreau M, Guertin F, Potvin J Y, etal. Parallel Tabu Seareh for Real-Time Vihicle Routingand Dispatching[J]. Transportation Scienee,1999,33(4):381-390.
[128]刘诚,陈治亚,封全喜.带软时间窗物流配送车辆路径问题的并行遗传算法[J].系统工程,2005,23(10):7-11.
[129]王楠，李世其，王峻峰.带时间窗的汽车总装线物料配送路径规划[J].工业工程,2012,15(2):94-99+120.
[130]李晋航,黄刚,贾艳.多模糊信息条件下的物料配送路径规划问题研究[J].机械工程学报,2011,47(1):124-131.
[131] Cheng R，Gen M. Vehicle routing problem with fuzzydue-time using genetic algorithms[J].Japanese Journal ofFuzzy Theory and Systems,1995,7(5):1050-1061.
[132] Huisman D, Freling R, Wagelmans Albert P M. A robust solution approach to the dynamicvehicle scheduling problem[J].Transportation Science,2004,38(4):447-458.
[133]王素欣,高利,崔小光,曹宏美,王亚军.多集散点车辆路径优化的混合算法[J].北京理工大学学报,2007,27(2):130-134.
[134] Guo B, Nonaka Y. Rescheduling and optimization of schedules considering machinefailures[J]. International Journal of Production Economics,1999,60-61:503-513.
[135] Leus R, Herroelen W. The complexity of machine scheduling for stabililty with a singledisrupted job[J]. Operations Research Letters,2005,33:151-156.
[136] Van de Vonder S, Demeulemeester E, Herroelen W. Proactive heuristic procedures for robustproject scheduling: an experimental analysis[J]. European Journal of Operational Research,2008,189(3):723-733.
[137] Sabuncuoglu I, Goren S. Hedging production schedules against uncertainty in manufacturingenvironment with a review of robustness and stability research[J]. International Journal ofComputer Integrated Manufacturing,2009,22(2):138-157.
[138] Wu S D, Storer R N, Chang P. One machine rescheduling heuristics with efficiency andstability as criteria[J]. Computers and Operations Research,20(1):1-14.
[139] Al-Fawzan M A, Haouari M. A bi-objective modal for robust resource constrained projectscheduling[J]. International Journal of Production Economics,2005,96:175-187.
[140] Dirk Briskorn, Joseph Leung, Michael Pinedo.2011. Robust Scheduling on Single MachineUsing Time buffers[J]. IIE Transactions,43:383-398.
[141] Van de Vonder S, Demeulemeester E, Herroelen W. A classification of predictive-reactiveproject scheduling procedures[J]. Journal of Scheduling,2007,10:195-207.
[142] Shi Z, Jeannot E, Dongarra J J. Robust task scheduling in non-deterministic heterogeneouscomputing systems[J]. IEEE International Conference on Cluster Computing,2006:1-10.
[143] Kobylanski P, Kuchta D. A note on the paper by M.A Al-Fawzan and M. Haouari about abi-objective problem for robust resource-constrained project scheduling[J]. International Journal ofProduction Economics,2007,203:283-293.
[144] O’Donovan R, Uzsoy R, Mckay K N. Predictable scheduling of a single machine withbreakdowns and sensitive jobs[J]. International Journal of Production Research,1999,37(18):4217-4233.
[145] Metha S V, Uzsoy R. Predictable scheduling of a single machine subject to breakdowns[J].Interantional Journal of Computer Integrated Manufacturing,1999,12(1):15-38.
[146] Kasperski A. Minimizing maximal regret in the single machine sequencing problem withmaximum lateness criterion[J]. Operations Research Letters,2005,33:431-436.
[147] Mehta S.V., Uzsoy R. Predictable scheduling of a job shop subject to breakdowns[J]. IEEETransactions on Robotic and Automation,1998,14(3):365-378
[148] Daniels R L, Kouvelis P. Robust scheduling to hedge against processing time uncertainty insingle-stage production[J]. Management Science,1995,41(2):363-376.
[149] Daniels R L, Carrillo J E. Beta-robust scheduling for single-machine system with uncertainprocessing times[J]. IIE Transaction,1997,29:977-985.
[150] Wu S D, Byeon E, Storer R H. A graph-theoretic decomposition of the job shop schedulingproblem to achieve scheduling robustness[J]. Operations Research,1999,47(1):113-124.
[151] Jensen M T. Improving robustness and flexibility of tardiness and total flowtime job shopsusing robustness measures[J]. Applied Soft Computing,2001,1:35-52.
[152] Jensen M T. Generating robust and flexible job shop schedules using genetic algorithms[J].IEEE Transactions on Evolutionary Computation,2003,7(3):275-288.
[153] Arigues C, Billaut J C, Esswein C. Maximization of solution flexibility for robust shopscheduling[J]. European Journal of Operational Research,2005,165:314-328.
[154] Gerwin D. Do’s and don’ts of computerized manufacturing[J]. Harvard Business Review,1982,60:107–116.
[155] Browne J, Dubois D, Rathmill K, et al. Classification of flexible manufacturing systems[J].FMS Magazine,1984:114–117.
[156] Lin G Y J, Solberg J J. Effectiveness of flexible routing control[J]. International Journal ofFlexible Manufacturing Systems,1991,3:189–211.
[157] Chan F T S. The effects of routing flexibility on a flexible manufacturing system[J].International Journal of Computer Integrated Manufacturing,2001,14:431–445.
[158] Kumar P R, Seidman T I. Dynamic instabilities and stabilization methods in distributedreal-time scheduling of manufacturing systems[J]. IEEE Transactions on Automatic Control,1990,35(3):289-298.
[159] Yeralan S, Tan B. A station model for continuous materials flow production systems[J].International Journal ofProduction Research,1997,35(9):2525-2541.
[160] Diamantidis A C,Papadopoulos C T, Vidalis M I. Exact analysis of a discrete materialthree-station one-buffer mergesystem with unreliable machines[J]. International Journal ofProduction Research,2004,42(4):651–675.
[161] Helber S, Mehrtens N. Exact analysis of a continuous material merge systemwith limitedbuffer capacity and three stations. International Series in Operations Research&ManagementScience,2003,60:85-121.
[162] Tan B. A three station merge system with unreliable stations and a shared buffer[J].Mathematical and Computer Modeling,2001,33:1011–1026.
[163] Di Mascolo M, David R, Dallery Y. Modeling and Analysis of assemblysystems withunreliable machines and finite buffers[J]. IIE Transactions,1991,23(4):315–331.
[164] Battinia D, Personaa A, Regattieri A. Buffer size design linked to reliability performance: Asimulative study[J]. Computers&Industrial Engineering,2009，56:1633–1641
[165] Sharifnia A. Stability and Performance of Distributed ProductionControl Methods based onContinuous-Flow Models[J]. IEEE Transactions on Automatic Control,1994,39(4):725-737.
[166] Tan B. Production Control of a Pull System With Production and Demand Uncertainty[J].IEEE Transactions on automatic control,2002,47(5):779-783.
[167] Sirikrai V, Yenradee P. Modified drum–buffer–rope scheduling mechanism for anon-identicalparallel machine flow shop with processing-time variation[J]. International Journal ofProduction Research,2006,44(17):3509–3531
[168]高柄志,吴狄,班晓娟,秦慧元.钢铁企业生产调度与仿真研究[J].计算机工程与科学,2011,33(9):157-163.
[169]李军,黄沫.基于方差法的生产物流平衡控制模型[J].工业工程,2008,11(6):50-53.
[170]张志峰,肖人彬.基于熵模型的生产物流系统运行评价[J].计算机集成制造系统,2010,16(4):810-816.
[171]俞燕.冷轧系统中生产物流平衡问题分析[J].物流技术,2008,27(3):90-92.
[172]刘明周,凌琳,唐娟.基于漂移瓶颈的制造车间生产批量/提前期研究[J].中国机械工程,2013,24(2):220-225.
[173]石全，米双山，王广彦，等.装备战伤理论与技术[M].北京：国防工业出版社,2007.
[174] Liu M., Tang J., Ge M.,et al. Dynamic prediction method of production logistics bottleneckbased on bottleneck index[J]. Chin J Mech Eng-En,2009,22(5):710-716.
[175]王国磊，林琳，钟诗胜.采用可用度理论的瓶颈设备前缓冲时间估计[J].哈尔滨工业大学学报,2009,41(11):53-58.
[176]唐娟.不确定环境下制造车间生产物流瓶颈漂移预测方法研究[D].合肥工业大学,2009.
[177]张建国,雷雨龙,王健,陆晓惠.基于BP神经网络的换档品质评价方法[J],吉林大学学报(工学版),2007,37(5):1019-1022.
[178]苏伟,刘景双,李方. BP神经网络在水资源承载能力预测中的应用[J].水利水电技术,2007,38(11):1-4.
[179] Panos K, Richard L.D, George V. Robust scheduling of a two-machine flow shop withuncertain processing times[J]. IIE Transaction,2000,32:421-432.
[180] Lu J C, Jinho Park, Qing Yang. Statistical inference of a time-to-failure distribution derivedfrom linear degradation data [J]. Technometrics,1997,39(4):391-400.
[181] Meeker M Q, Escobar L A. Statistical Methods for reliability data[M]. New York: JohnWiley&Sons, Inc.1998.
[182]苏文斌,刘兆栋,孙俊成,王艳丽.基于敏感性分析的注塑成型工艺参数优化[J].塑性工程学报,2009,16(3):202-206.
[183]李志刚.基于DEMATEL的制造业企业动态平衡计分卡的应用研究[D].哈尔滨：哈尔滨理工大学,2009.
[184]易树平，郭伏.基础工业工程[M].北京:机械工业出版社，2009.
[185]莫巨华,黄敏,王兴伟.串行饱和生产线模糊控制方法的研究[J].控制工程,2007,14(1):34-36+81.
[186] Carlier J, Pinson, E. An algorithm for solvingthe job-shop problem[J]. Management Science,1989,35(2):164–176.
[187] Nyhuis, P. and Wiendahl, H P. Logistische kennlinien: Grundlagenwerkzeuge undanwendugen,2003,2nd edition. Berlin: Springer.(German)
[188] Matthias Hiisig. Online Workload Control with Shifting Bottlenecksand Due DateConstraints[C]. Proceedings of2009IEEE International Symposium on Assembly andManufacturing,2009, Suwon, Korea:17-20.
[189] Kim, S., Davis, K.R. and Cox III, J.F. III,2003. An investigation of output flow control,bottleneckflow control and dynamic flow control mechanisms in various simple linescenarios[J].Production Planning&Control,14(1):15–32.
[190] Nabil Mikatia. Dependence of lead time on batch size studied by a system dynamics model[J].International Journal of Production Research,2010,48(18):5523–5532.
[191]陈淮莉，张洁，马登哲.一种新型供应链物料需求计划模型[J].上海交通大学学报，2005,39(1):98-104.
[192] Zijm W H M, Buitenhek R. Capacity planning and lead time management[J]. InternationalJournal of Production Economics,1996,46-47(1):165-179
[193] Koo Pyung-Hoi, Lee Woon-Seek, Kim Youngjin. Production batch sizing at multi-productbottleneck machines[C]//2009International Conference on Computers and Industrial Engineering,Troyes, France,6-9July,2009:814-818.
[194] Matuszek J, Mleczko J. Production control in moving bottlenecks in conditions of unit andsmall-batch production[J]. Bulletin of the Polish Academy of Sciences: Technical Sciences,2009,57(3):229-239.
[195] Kooa Pyung-Hoi, Bulfinb Robert, Koh Shie-Gheun. Determination of batch size at abottleneck machine in manufacturing systems[J]. International Journal of Production Research,2007,45(5):1215–1231.
[196]华兴（美）.排队论与随机服务系统[M].上海：上海翻译出版公司，1987.
[197]焦李成,尚荣华,马文萍等.多目标优化免疫算法、理论和应用[M].北京：科学出版社.2010.
[198] Coello C A, Pulido G T, Lechuga M S. Handing multiple objectives with particle swarmoptimization. IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[199] Deb K, Pratap A. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II [J].IEEETransactions on Evolutionary Computation,2002,6(2):182-197.
[200] Deb K, Goldberg D E. An investigation of niche and species formation in genetic functionoptimization [C]. In the Third International Conference on Genetic Algorithms, Piscataway, NJ,1989:42-50.
[201]李晋航.混流制造车间物料配送调度优化研究[D].武汉：华中科技大学,2009.
[202] Kolen A, Rinnoy K A, Trienkens H. Vehicle Routing With Time Windows[J]. OperationResearch,1987,35:266-273.
[203]王文义,任刚.多种群退火贪婪混合遗传算法[J].计算机工程与应用,2005,23:60-62.
[204]孟佳娜,王立宏.具有自识别能力的遗传算法求解旅行商问题[J].计算机工程与应用,2006,13:51-53.
[205] Wu S.D., Storer R.N., Chang P. One-machine rescheduling heuristics with efficiency andstability as criteria[J]. Computers and Operations Research,1993,20(1):1-14.
[206] Wu S.D., Storer R.N., Chang P. One-machine rescheduling heuristics with efficiency andstability as criteria[J]. Computers and Operations Research,1993,20(1):1-14.
[207] Goren S, Sabuncuoglu I. Optimization of schedule robustness and stability under randommachine breakdowns and processing time variability[J]. IIE Transaction,2010,42:203-220.
[208] Leus R, Herroelen W. Scheduling for stability in single-machine production systems[J].Journal of Scheduling,2007,10:223-235.
[209] Van De Vonder S, Demeulemeester E, Herroelen W, et al. The trade-off between stability andmakespan in resource-constrained project scheduling[J]. International Journal of ProductionResearch,2006,44(2):215-236.
[210] Kouveilis P, Daniels R L, Vairaktarakis G. Robust scheduling of a two-machine two-shopwith uncertain processing times[J]. IIE Transactions,2000,32:421-432.
[211] Briskorn D, Leung J. Pinedo M. Robust scheduling on a single machine using time buffers[J].IIE Transaction,2011,43:383-398.
[212] Lu C, Lin S, Ying K. Robust scheduling on a single machine to minimize total flow time[J].Computers and Operation Research,2012,39:1682-1691.
[213] Goren S, Sabuncuoglu I. Robustness and stability measures for scheduling: single-machineenvironment[J]. IIE Transactions,2008,40:66-83.
[214] Du C, Pinedo M. A note on minimizing the expected makespan in flow shops subject tobreakdowns[J]. Naval Research Logistics,1995,42:1251-1262.
[215] Allahverdi A, Mittenthal J. Scheduling on a two-machine owshop subject to randombreakdowns with a makespan objective function[J]. European Journal of Operational Research,1995,81:376-387.
[216] Brige J, Glazebrrok K. Assessing the effects of machine breakdown in stochasticscheduling[J]. Operations Research Letters,1988,7:267-271.
[217] Yang J, Yu G. On the robust single machine scheduling problem[J]. Journal of CombinationOptimization,2002,6:17-33.
[218] Allahverdia A, Aydilek H. Heuristics for the two-machine flow shop scheduling problem tominimize makespan with bounded processing times[J]. International Journal of ProductionResearch,2010,48(21):6367-6385.
[219] Tao Jipin, Chao Zhijun, Xi Yugeng, et al. An optimal semi-online algorithm for a singlemachine scheduling problem with bounded processing time[J]. Information Processing Letters,2010,110(8-9):325-330.
[220] Kouvelis P, Yu G. Robust Discrete Optimization and Its Applications [M]. Boston: Kluwer,1997.
[221] Gentle, J. E. Random Number Generation and Monte Carlo Methods (2nd Edition)[M],2003,Springer.
[222] Leon V J, Wu S D, Storer R H. Robustness Measures and Robust Scheduling for JobShops[J]. IIE Transactions,1994,26(5):32-43.
[2]王之泰.现代物流管理[M].北京：中国工人出版社,2001.
[3] Hopp W J, Spearman M L. Factory physics[M]. Irwin McGraw-Hill,2nd edition,2000.
[4] Ritzman L P, Krajewski L J. Comparison of material requirements planning and reorder pointsystem[J]. Simulation Councils Proceedings Series,1984,12(2):47-54.
[5] Amin, M, Altiok T. Control policies for multi-product multi-stage manufacturing systems: anex-perimental approach[J]. International Journal of Production Research,1997,35(1):201-223.
[6] Zijm W H M, Buitenhek R. Capacity planning and lead time management[J]. InternationalJournal of Production Economics,1996,46-47:165-179.
[7] Gahagan S M, Herrmann J W. Finding the optimal production control policy using theproduction control framework [C]. Proceedings of the2005Winter Simulation Conference,2005:1418-1427.
[8]王辉.雪铁龙公司拉动式物流基本概念[J].物流技术,1996,4.
[9] Spearman M L, Wookdruff D L, Hopp W J. CONWIP: a pull alternative to kanban[J].International Journal of Production Research,1990,28(5):879-894.
[10] Bonvik A M, Couch C, Gershwin S B. A comparison of production-line control mechanisms[J].International Journal of Production Research,1997,35(3):789-804.
[11] Spearman M L, Zazanis M A. Push and pull production systems: issue and comparison[J].Operations Research,1992,40(3):521-532.
[12] A. Agnetis, A. Pacifici, F. Rossi, M. Lucertini, S. Nicoletti, F. Nicolò, G. Oriolo, D. Pacciarelli,E. Pesaro. Scheduling of flexible flow lines in an automobile assembly plant[J], European Journalof Operational Research,1997,97(2):348-362.
[13] Sader B H. Development and application of a new modeling technique for production controlschemes in manufacturing systems[D]. Brigham Young University,2005.
[14] Spearman M L. Customer service in pull production system[J]. Operations Research,1992,40(5):948-958.
[15] Bonvik A M, Dallery Y, Gershwin S B. Approximate analysis of production systems operatedby a conwip/finite buffer hybrid control policy[J]. International Journal of Production Research,2000,38(13):2845-2869.
[16] Goldratt E. The goal: a process of ongoing improvement[M]. Great Barrington, MA: NorthRiver Press Inc,1984.
[17] Cook D P. A simulation comparison of traditional, JIT, and TOC manufacturing systems in aflow shop with bottlenecks[J]. Production and Inventory Management,1994,35:73-78.
[18] Guide V D R. Scheduling using drum-buffer-rope in a remanufacturing environment[J].International Journal of Production Research,1996,34(4):1081-1091.
[19] Altug M S. Design and performance evaluation of mixed MRP, Kanban, and CONWIPproduction systems using emulated flexible manufacturing laboratory[D]. University of Florida,Gainesville,1997.
[20] Blake N, Hellberg R. Relevance lost? A critical discussion of different cost accountingprinciples in connection with decision making for both short and long term productionscheduling[J]. International Journal of Production Economics,1991,24:1-18.
[21] Srivatsan N.. Synthesis of optimal policies for stochastic manufacturing systems[D].Massachusetts Institute of Technology,1993.
[22] Gershwin, S.B. System analysis, design and control: Unification and decomposition[C],Second Aegean International Conference on Modeling of Manufacturing Systems, Tinos Island,Greece,1999.
[23] Gershwin S B. Design and operation of manufacturing systems-the control point policy[J].IIE Transactions,2000,32(2):891-906.
[24] Deshpande S P. A scheduling policy experiment for lean implementation[D]. MassachusettsInstitute of Technology,1999.
[25] Gzouli O. Comparison of scheduling policies by simulation[D]. Massachusetts Institute ofTechnology,2000.
[26] Yong M S. Simulation of real-time scheduling policies in multi-product, make-to-ordersemiconductor facilities[D]. Massachusetts Institute of Technology,2001.
[27] Kim C. Comparison and optimization of control policies in automobile manufacturing systemsby simulation[D]. Massachusetts Institute of Technology,2004.
[28] Jason Ardon-Finch B A, Riham K. Control point policy: part1-efficiency withinmake-to-order environments[J]. International Journal of Production Research,2008,46(11):2927-2943.
[29] Breithaupty J W. Controlling production dynamics-managing uncertainties with automaticproduction control[J]. International Journal of Production Research,2000,38(13):4235-4246.
[30] Christoph Roser, Masaru Nakano, Minoru Tanaka. A Practical Bottleneck DetectionMethod[A]. Proceedings of the2001Winter Simulation Conference, Arlington,2001:949-953.
[31] Christoph Roser, Masaru Nakano, Minoru Tanaka. Shifting Bottleneck Detection[C].Proceedings of the2002Winter Simulation Conference, San Diego,2002:1079-1086.
[32] Christoph Roser, Masaru Nakano, Minoru Tanaka.Comparison of Bottleneck DetectionMethods for AGV Systems [A]. Proceedings of the2003Winter Simulation Conference, NewOrleans,2003:1192-1198.
[33] Patrick Faget, Ulf Eriksson, Frank Herrmana. Appling Discrete Event Simulation and anAtuomated Bottleneck Analysis as an Aid to Detect Running Production Constraints[C].Proceedings of the2005Winter Simulation Conference,2005:1401-1407.
[34] ShiNung Ching, Semyon M Meerkov, Liang Zhang.Assembly systems with non-exponentialmachines: Throughput and bottlenecks [J]. Nonlinear Analysis: Theory, Methods&Applications,2008,69(31):911-917.
[35] Li L, Chang Q, Ni J. Data driven bottleneck detection of manufacturing systems [J].International Journal Of Production Research,2009,47(18):5019-5036.
[36] Chang Q, Ni J, Bandyopadhyay P, Biller S, Xiao G. Supervisory Factory Control Based onReal-Time Production Feedback[J]. Journal of Manufacturing Science and Engineering,2007,6(129):653-660.
[37] Pegels C.Carl, Watrous Craig. Application of the theory of constraints to a bottleneckoperation in a manufacturing plant [J]. Journal of Manufacturing Technology Management,2005,3(16):302-311.
[38] Muthiah KMN, Huang SH. Overall throughput effectiveness(OTE) metric for factory levelperformance monitoring and bottleneck detection[J]. Internatioal Journal of Production Research,2007,45(20):4753-4769.
[39] Muthiah KMN, Huang SH, Sangeetha Mahadevan. Automating factory performancediagnostics using overall throughput effectiveness (OTE) metric[J]. The International Journal ofAdvanced Manufacturing Technology,2008,36:811-824.
[40] Ingemansson A, Torbjo¨rn Ylipa¨a¨, Gunnar S.Bolmsjo. Reducing bottlenecks in amanufacturing data collection and discrete-event simulation[J]. Journal of ManufacturingTechnology Management,2005,16(6):615-628.
[41] Babu T R, Rao K. S. P., Maheshwaran C. U. Application of TOC embedded ILP for increasingthroughput of Production lines [J]. International Journal of Manufacturing Technology andManagement,2007,33(7-8):812-818.
[42] Richard A R. Applying the TOC five-step focusing process in the service sector-A bankingsubsystem [J].Managing Service Quality,2007,17(02):209-234.
[43] Miyashita K, Rajesh G. Multiagent coordination for controlling complex and unstablemanufacturing processes[J]. Expert Systems with Applications,2010,37(03):1836-1845.
[44] Choi S H, Yang F Y. A filter search algorithm based on machine-order space for job-shopscheduling problems [J]. Journal of Manufacturing Technology Management,2006,3(17):376-392.
[45] Chen Chun-Lung, Chen Chuen-Lung. A Heuristic Method for a Flexible Flow Line withUnrelated Parallel Machines Problem[A]. Robotics,Automation and Mechatronics,2006IEEEConference, Chicago,2006:1-4.
[46]高文会.约束理论的瓶颈识别研究[J].西安石油大学学报(社会科学版),2008,(01):51-56.
[47] Sawik T. Multi-objective due-date setting in a make-to-order environment[J]. InternationalJournal Of Production Research,2009,47(22):6205-6213.
[48] Rajakumar S, Arunachalam V P, Selladurai V. Workflow balancing in parallel machinescheduling with precedence constraints using genetic algorithm[J]. Journal of ManufacturingTechnology Management,2006,17(02):239-254.
[49] Zhang R, Wu C. Bottleneck identification procedures for the job shop scheduling problem withapplications to genetic algorithms[J]. The International Journal of Advanced ManufacturingTechnology,2009,6(42):1153-1164.
[50]李晓红,周炳海.晶圆制造过程中动态瓶颈设备的实时调度[J].上海交通大学学报,2008,(04):599-602.
[51]乔非,许潇红,方明,吴启迪.半导体晶圆生产线调度的性能指标体系研究[J].同济大学学报(自然科学版),2007,(04):537-542.
[52]徐云晴，邵炜，李树刚.基于SAP和TOC的瓶颈识别[J].苏州大学学报(自然科学版),2008,24(04):63-68.
[53]陈菊红,史成东,杨阳.供应链产品物流过程模型及时间瓶颈的识别[J].中国管理科学,2009,17(06):63-69.
[54] Wang S X. A look-ahead heuristic algorithm for dynamic identify bottleneck machine[J].Industrial Engineering Journal,2008,7(11):127-131.
[55]郭永辉.基于产出速度的Job-shop型企业生产作业计划与控制方法[J].工业工程,2008,11(05):86-89.
[56]周夫利，韩文民，刘志勇，龚俏巧.基于约束理论的车间调度仿真系统研究与设计[J].自动化仪表,2009,30(05):11-13.
[57]许一敏.基于瓶颈的BTO供应链响应时间优化与控制研究[D].华中科技大学,2009.
[58]邹律龙,侯东亮.面向订单的瓶颈资源识别与单机成组作业调度[J].现代机械,2009,(02):91-93.
[59] Carlos D P, Jairo R M T, Milton J A D, Maria C H H. Scheduling jobs on a k-stage flexibleflow-shop[J]. Annals of Operations Research,2008,11(164):29-40.
[60] Milton J A-D, Carlos D P-A. Scheduling Jobs on a K_Stage Flexible Flow Shop Using aTOC-Based (Bottleneck) Procedure: Proceeding of the2004Systems and Information EngineeringDesign Symposium[C].2004:295-298.
[61]沈妙妙,陈雷雷,鲁建厦.生产瓶颈转移性影响因素的经济计量研究[J].上海电机学院学报,2008,(02):139-143.
[62]王国磊，林琳，钟诗胜.采用可用度理论的瓶颈设备前缓冲时间估计[J].哈尔滨工业大学学报,2009,41(11):53-58.
[63]杨丽艳，钱省三.半导体晶圆生产系统瓶颈识别与管理对策[J].现代管理,2008,32(12):1021-1024.
[64]张怀，江志斌，郭乘涛.面向瓶颈的半导体晶圆制造系统派工策略及参数优化[J].上海交通大学学报，2007,41(08):1252-1257.
[65] Liao C J. Optimal release time on a stochastic single-machine[J]. International Journal ofSystems Science,1992,23(10):1693-1701.
[66] Gong L G, Kok T, Ding J. Optimal lead-times planning in a serial production system[J].Management Science,1994,40(5):629-632.
[67] Buss A H, Rosenblatt M J. Activity delay in stochastic project networks[J]. OperationResearch,1997,45(1):126-139.
[68] Elmaghraby S E, Ferreira A A, Tavares L V. Optimal start times under stochasitc activitydurations[J]. International Journal of Production Economics,2000,64:153-164.
[69] Elmaghraby S E. On the optimal release time of jobs with random processing times withextensions to other criteria[J]. International Journal of Production Economics,2001,74:103-113.
[70] HomemdeMell T, Shapiro A, Spearman M L. Finding optimal material release times usingsimulation-based optimisation[J]. Management Science,1999,45(1):86-102
[71] Hasan C N, Spearman M L. Optimal material release times in stochastic productionenvironments[J]. International Journal of Production Research,1999,37(6):1201-1216.
[72] Song D P, Hicks C, Earl C F. Setting planned job release times in stochastic assembly systemswith resource constraints[J]. International Journal of Production Research,2001,39(6):1289-1301.
[73] Song D P. Raw material release time control for complex make-to-orderproducts withstochastic processing times[J]. International Journal of Production Economics,2006,103:371–385.
[74] Nahavandi N. CWIPL II, a mechanism for improving throughput and lead time in unbalancedflow line[J]. International Journal of Production Research,2009,47(11):2921-2941.
[75]崔南方,刘英姿,严仕锋.物料需求计划提前期设置的改进方法[J].华中科技大学学报(自然科学版),2003,31(5):95-97.
[76] Missbauer R. Models of the transient behaviour of production units to optimizethe aggregatematerial flow[J]. Int. J. Production Economics,2009,118:387–397.
[77] Le Q S, Knapp G M. Incorporating fuzzy logic admission control in simulation models[C].Winter Simulation Conference Proceedings,2003,2:1276-1280.
[78] Cheng C B, Liu Z Y, Chiu S W, Cheng C J. Applying fuzzy logic control to inventory decisionin a supply chain[J]. WSEAS Transactions on System,2006,5(8):1822-1829.
[79] Sarker B R, Parija G R. Optimal batch size and raw material ordering policy for a productionsystem with a fixed-interval lumpy demand delivery system[J]. European Journal of OperationalResearch,1996,89:593-608.
[80] Sarker R A, Khana L R. An optimal batch size for a production system periodic deliverypolicy[J]. Computers&Industrial Engineering,1999,37:711-730.
[81] Khana L R, Sarker R A. An optimal batch size for a JIT manufacturing system[J]. Computers&Industrial Engineering,2002,42:127-136.
[82] Grubbstrom R W, Bogataj M, Bogataj L. Optimal lotsizing within MRP Theory[J]. AnnualReviews in Control.2010,34:89–100.
[83] Biller S, Meerkov S M, Yan C B. Raw material release rates to ensure desired production leadtime in Bernoulli serial lines[J]. International Journal of Production Research,2013,51:14,4349-4364.
[84]郝刚,吴功宜.根据日产量动态调整批量和提前期的物料需求计划研究[J].计算机集成制造系统,2005,11(9):1267-1271.
[85]武保全,孙棣华.基于泛在制造信息的动态物料配送批量决策[J].现代制造工程,2013,1:16-19+37.
[86] Rosenblatt M J. Dynamics of plant layout [J]. Management Science,1986,32(1):76–86.
[87] Rosenblatt M J, Lee H L. A robustness approach to facilities design[J]. International Journal ofProduction Research,1987,25:479–486.
[88] Balakrishnan J, Jacobs F R, Venkataramanan M A. Solutions for the constrained dynamicfacility-layout problem[J]. European Journal of Operational Research,1992,57:280–286.
[89] Palekar U S, Batta R, Bosch R M, et al. Modeling uncertainties in plant layout problems[J].European Journal of Operational Research,1992,63:347–359.
[90] Urabm T L. Computational performance and efficiency of lower-bound procedures for thedynamic facility layout problem[J]. European Journal of Operational Research,1992,57:271–279.
[91] Urabm T L. A heuristic for the dynamic facility layout problem[J]. IIE Transactions,1993,25(4):57–63.
[92] Urabm T L. Solution procedures for the dynamic facility layout problem[J]. Annals ofOperations Research,1998,76:323–342.
[93] Kouveils P, Kiran A S. Single and multiple period layout models for automated manufacturingsystems[J]. European Journal of Operational Research,1991,52:300–314.
[94] Kouvelis, P., Kurawarwala, A. A., and Gutierrez, G. J. Algorithms for robustsingle andmultiple period layout planning for manufacturing systems[J]. European Journalof OperationalResearch,1992,63:287-303.
[95] Yang T. and Peters B A. Flexible machine layout design for dynamic and uncertain productionenvironments[J]. European Journal of Operational Research,1998,108:49–64.
[96] Kochhar J S, Heragu S S. Facility layout design in a changing environment[J]. InternationalJournal of Production Research,1999,37:2429–2446.
[97] Pillaia V. M, Hunagunda I B, Krishnan K K. Design of robust layout for Dynamic PlantLayout Problems[J]. Computers&Industrial Engineering,2011,61:813–823.
[98] Lee T S, Moslemipour G. Intelligent design of a flexible cell layout with maximum stability ina stochastic dynamic situation[C].//1st International Conference on Intelligent Robotics,Automation and Manufacturing, IRAM2012, Kuala Lumpur, Malaysia, November28-November30,2012:398-405.
[99] Curry G L, Peters B A, Lee M. Queueing network model for a class of material-handlingsystems[J]. InternationalJournal of Production Research,2003,41(16):3901–3920.
[100] Sukhotu V. Material handling system models and block layout approaches for facilitydesign[D]. Texas A&M University,2002.
[101] Sukhotu V, Peters B A. Modelling of material handling systems for facilitydesign inmanufacturing environments with job-specific routing[J]. International Journal of ProductionResearch,2012,24(15):7285–7302.
[102] Fisher M. Vehicle routing[M]. In: Handbooks in Operations Research and ManagementScience, North-Holland, Amsterdam,1995,8:1-33.
[103] Renaud J, Laporte G, Boctor F F. A tabu search heuristic for the multi-depot vehicle routingproblem[J]. Computers and Operations Research,1996,23(3):229-235.
[104] Baker B M, Ayechew M A. A genetic algorithm for the vehicle routing problem[J].Computers&Operations Research,2003,30:787-800.
[105]郎茂祥.用单亲遗传算法求解配送车辆调度问题的研究[J].交通与计算机,2006,(01):119-121.
[106] Moghaddam R T, Safaei N, Gholipour Y. A hybrid simulated annealing for capacitatedvehicle routing problems with the independent route length[J]. Applied Mathematics andComputation,2006,176:445-454.
[107] Osman I H. Metastrategy simulated annealing and tabu search algorithms for the vehiclerouting problem[J]. Annual of Operations Research,1993,(41):421—451.
[108] Archetti C, Speranza M G, Savelsbergh M W P. An optimization-based heuristic for the splitdelivery vehicle routing problem[J]. Transportation Science,2008,42(1):22-31.
[109] Potvin J Y, Kervahut T, Garcia B L, et al. The vehicle routing problem with time windowspartⅠ: Tabu Search[J]. Informs Journal on Computing,1996,8(2):158-164.
[110] Favaretto D, Morette E, Pellegrini P. Ant colony system for a VRP with multiple timewindows and multiple visits[J]. Journal of Interdisciplinary Mathematics,2007,10(2):263-284.
[111] Donati A V, Montemanni R, Casagrande N, et al. Time dependent vehicle routing problemwith a multi ant colony system[J]. European Journal of Operational Research,2008,185:1174-1191.
[112] Yannis M, Magdalene M. A hybrid genetic-particle swarm optimization algorithm for Tehranvehicle routing problem[J]. Expert Systems with Application,2010,37:1446-1455.
[113]张丽艳,庞小红,夏蔚军,等.带时间窗车辆路径问题的混合粒子群算法[J].上海交通大学学报,2006,40(11):1890-1894+1900.
[114] Tan K C, Chew Y H, Lee L H. A hybrid multi-objective evolutionary algorithm for solvingvehicle routing problem with time windows[J]. Computational Optimization and Applications,2006,34:115-151.
[115] Russell B, Pascal V H. A two stage hybrid algorithm for pickup and delivery vehicle routingproblems with time windows[J]. Computers and Operations Research,2006,33:875-893.
[116]赵若彤.一种物流配送车辆路径智能优化算法研究[J].计算机与数字工程,2013,41(4):529-531+537.
[117]李敏,郭强,刘红丽.多车场多配送中心的物流配送问题研究[J].计算机工程与应用,2007,43(8):202-204+208.
[118] Hadjar A, Marcotte O, Soumis F. A branch-and-cut algorithm for the multiple depot vehiclescheduling problem[J]. Operations Research,2006,54(1):130-149.
[119] Dondo R,Cerda J. A cluster-based optimization approach for the multi-depot heterogeneousfleet vehicle routing problem with time windows[J]. European Journal of Operational Research,2007,176:1478-1507.
[120]于滨,靳鹏欢,杨忠振.两阶段启发式算法求解带时间窗的多中心车辆路径问题[J].系统工程理论与实践,2012,32(8):1793-1800.
[121] Tedorovid D, Pavkovic G. The fuzzy set theoryapproach to the vehicle routing problem whendemand atnodes is uncertain[J]. Fuzzy Sets and Systems，1996，82(3)：307-317.
[122]廖杨喆,王烨东,王一晗.基于专家系统的单天车配料实时路径优化与管理[J].价值工程,2010,32:200-201.
[123]周长峰,谭跃进,廖良才.实时条件下多车辆路径与调度[J].系统工程，2006,24(5):36-41.
[124]樊建华,王秀峰.随机需求多车辆路径问题的重优化算法[J].南开大学学报(自然科学版)，2008,41(2):103-107.
[125]李军.有时间窗的车辆路线安排问题的启发式算法.系统工程,1996,114(5):45-50。
[126] Taillard E, Badeau P, Gendreau M, et al. A Thbu Seareh Heuristie for the Vehiele RoutingProblem with Soft time Windows[J]. Transportation Scienee,1997,31(2):170-186.
[127] Gendreau M, Guertin F, Potvin J Y, etal. Parallel Tabu Seareh for Real-Time Vihicle Routingand Dispatching[J]. Transportation Scienee,1999,33(4):381-390.
[128]刘诚,陈治亚,封全喜.带软时间窗物流配送车辆路径问题的并行遗传算法[J].系统工程,2005,23(10):7-11.
[129]王楠，李世其，王峻峰.带时间窗的汽车总装线物料配送路径规划[J].工业工程,2012,15(2):94-99+120.
[130]李晋航,黄刚,贾艳.多模糊信息条件下的物料配送路径规划问题研究[J].机械工程学报,2011,47(1):124-131.
[131] Cheng R，Gen M. Vehicle routing problem with fuzzydue-time using genetic algorithms[J].Japanese Journal ofFuzzy Theory and Systems,1995,7(5):1050-1061.
[132] Huisman D, Freling R, Wagelmans Albert P M. A robust solution approach to the dynamicvehicle scheduling problem[J].Transportation Science,2004,38(4):447-458.
[133]王素欣,高利,崔小光,曹宏美,王亚军.多集散点车辆路径优化的混合算法[J].北京理工大学学报,2007,27(2):130-134.
[134] Guo B, Nonaka Y. Rescheduling and optimization of schedules considering machinefailures[J]. International Journal of Production Economics,1999,60-61:503-513.
[135] Leus R, Herroelen W. The complexity of machine scheduling for stabililty with a singledisrupted job[J]. Operations Research Letters,2005,33:151-156.
[136] Van de Vonder S, Demeulemeester E, Herroelen W. Proactive heuristic procedures for robustproject scheduling: an experimental analysis[J]. European Journal of Operational Research,2008,189(3):723-733.
[137] Sabuncuoglu I, Goren S. Hedging production schedules against uncertainty in manufacturingenvironment with a review of robustness and stability research[J]. International Journal ofComputer Integrated Manufacturing,2009,22(2):138-157.
[138] Wu S D, Storer R N, Chang P. One machine rescheduling heuristics with efficiency andstability as criteria[J]. Computers and Operations Research,20(1):1-14.
[139] Al-Fawzan M A, Haouari M. A bi-objective modal for robust resource constrained projectscheduling[J]. International Journal of Production Economics,2005,96:175-187.
[140] Dirk Briskorn, Joseph Leung, Michael Pinedo.2011. Robust Scheduling on Single MachineUsing Time buffers[J]. IIE Transactions,43:383-398.
[141] Van de Vonder S, Demeulemeester E, Herroelen W. A classification of predictive-reactiveproject scheduling procedures[J]. Journal of Scheduling,2007,10:195-207.
[142] Shi Z, Jeannot E, Dongarra J J. Robust task scheduling in non-deterministic heterogeneouscomputing systems[J]. IEEE International Conference on Cluster Computing,2006:1-10.
[143] Kobylanski P, Kuchta D. A note on the paper by M.A Al-Fawzan and M. Haouari about abi-objective problem for robust resource-constrained project scheduling[J]. International Journal ofProduction Economics,2007,203:283-293.
[144] O’Donovan R, Uzsoy R, Mckay K N. Predictable scheduling of a single machine withbreakdowns and sensitive jobs[J]. International Journal of Production Research,1999,37(18):4217-4233.
[145] Metha S V, Uzsoy R. Predictable scheduling of a single machine subject to breakdowns[J].Interantional Journal of Computer Integrated Manufacturing,1999,12(1):15-38.
[146] Kasperski A. Minimizing maximal regret in the single machine sequencing problem withmaximum lateness criterion[J]. Operations Research Letters,2005,33:431-436.
[147] Mehta S.V., Uzsoy R. Predictable scheduling of a job shop subject to breakdowns[J]. IEEETransactions on Robotic and Automation,1998,14(3):365-378
[148] Daniels R L, Kouvelis P. Robust scheduling to hedge against processing time uncertainty insingle-stage production[J]. Management Science,1995,41(2):363-376.
[149] Daniels R L, Carrillo J E. Beta-robust scheduling for single-machine system with uncertainprocessing times[J]. IIE Transaction,1997,29:977-985.
[150] Wu S D, Byeon E, Storer R H. A graph-theoretic decomposition of the job shop schedulingproblem to achieve scheduling robustness[J]. Operations Research,1999,47(1):113-124.
[151] Jensen M T. Improving robustness and flexibility of tardiness and total flowtime job shopsusing robustness measures[J]. Applied Soft Computing,2001,1:35-52.
[152] Jensen M T. Generating robust and flexible job shop schedules using genetic algorithms[J].IEEE Transactions on Evolutionary Computation,2003,7(3):275-288.
[153] Arigues C, Billaut J C, Esswein C. Maximization of solution flexibility for robust shopscheduling[J]. European Journal of Operational Research,2005,165:314-328.
[154] Gerwin D. Do’s and don’ts of computerized manufacturing[J]. Harvard Business Review,1982,60:107–116.
[155] Browne J, Dubois D, Rathmill K, et al. Classification of flexible manufacturing systems[J].FMS Magazine,1984:114–117.
[156] Lin G Y J, Solberg J J. Effectiveness of flexible routing control[J]. International Journal ofFlexible Manufacturing Systems,1991,3:189–211.
[157] Chan F T S. The effects of routing flexibility on a flexible manufacturing system[J].International Journal of Computer Integrated Manufacturing,2001,14:431–445.
[158] Kumar P R, Seidman T I. Dynamic instabilities and stabilization methods in distributedreal-time scheduling of manufacturing systems[J]. IEEE Transactions on Automatic Control,1990,35(3):289-298.
[159] Yeralan S, Tan B. A station model for continuous materials flow production systems[J].International Journal ofProduction Research,1997,35(9):2525-2541.
[160] Diamantidis A C,Papadopoulos C T, Vidalis M I. Exact analysis of a discrete materialthree-station one-buffer mergesystem with unreliable machines[J]. International Journal ofProduction Research,2004,42(4):651–675.
[161] Helber S, Mehrtens N. Exact analysis of a continuous material merge systemwith limitedbuffer capacity and three stations. International Series in Operations Research&ManagementScience,2003,60:85-121.
[162] Tan B. A three station merge system with unreliable stations and a shared buffer[J].Mathematical and Computer Modeling,2001,33:1011–1026.
[163] Di Mascolo M, David R, Dallery Y. Modeling and Analysis of assemblysystems withunreliable machines and finite buffers[J]. IIE Transactions,1991,23(4):315–331.
[164] Battinia D, Personaa A, Regattieri A. Buffer size design linked to reliability performance: Asimulative study[J]. Computers&Industrial Engineering,2009，56:1633–1641
[165] Sharifnia A. Stability and Performance of Distributed ProductionControl Methods based onContinuous-Flow Models[J]. IEEE Transactions on Automatic Control,1994,39(4):725-737.
[166] Tan B. Production Control of a Pull System With Production and Demand Uncertainty[J].IEEE Transactions on automatic control,2002,47(5):779-783.
[167] Sirikrai V, Yenradee P. Modified drum–buffer–rope scheduling mechanism for anon-identicalparallel machine flow shop with processing-time variation[J]. International Journal ofProduction Research,2006,44(17):3509–3531
[168]高柄志,吴狄,班晓娟,秦慧元.钢铁企业生产调度与仿真研究[J].计算机工程与科学,2011,33(9):157-163.
[169]李军,黄沫.基于方差法的生产物流平衡控制模型[J].工业工程,2008,11(6):50-53.
[170]张志峰,肖人彬.基于熵模型的生产物流系统运行评价[J].计算机集成制造系统,2010,16(4):810-816.
[171]俞燕.冷轧系统中生产物流平衡问题分析[J].物流技术,2008,27(3):90-92.
[172]刘明周,凌琳,唐娟.基于漂移瓶颈的制造车间生产批量/提前期研究[J].中国机械工程,2013,24(2):220-225.
[173]石全，米双山，王广彦，等.装备战伤理论与技术[M].北京：国防工业出版社,2007.
[174] Liu M., Tang J., Ge M.,et al. Dynamic prediction method of production logistics bottleneckbased on bottleneck index[J]. Chin J Mech Eng-En,2009,22(5):710-716.
[175]王国磊，林琳，钟诗胜.采用可用度理论的瓶颈设备前缓冲时间估计[J].哈尔滨工业大学学报,2009,41(11):53-58.
[176]唐娟.不确定环境下制造车间生产物流瓶颈漂移预测方法研究[D].合肥工业大学,2009.
[177]张建国,雷雨龙,王健,陆晓惠.基于BP神经网络的换档品质评价方法[J],吉林大学学报(工学版),2007,37(5):1019-1022.
[178]苏伟,刘景双,李方. BP神经网络在水资源承载能力预测中的应用[J].水利水电技术,2007,38(11):1-4.
[179] Panos K, Richard L.D, George V. Robust scheduling of a two-machine flow shop withuncertain processing times[J]. IIE Transaction,2000,32:421-432.
[180] Lu J C, Jinho Park, Qing Yang. Statistical inference of a time-to-failure distribution derivedfrom linear degradation data [J]. Technometrics,1997,39(4):391-400.
[181] Meeker M Q, Escobar L A. Statistical Methods for reliability data[M]. New York: JohnWiley&Sons, Inc.1998.
[182]苏文斌,刘兆栋,孙俊成,王艳丽.基于敏感性分析的注塑成型工艺参数优化[J].塑性工程学报,2009,16(3):202-206.
[183]李志刚.基于DEMATEL的制造业企业动态平衡计分卡的应用研究[D].哈尔滨：哈尔滨理工大学,2009.
[184]易树平，郭伏.基础工业工程[M].北京:机械工业出版社，2009.
[185]莫巨华,黄敏,王兴伟.串行饱和生产线模糊控制方法的研究[J].控制工程,2007,14(1):34-36+81.
[186] Carlier J, Pinson, E. An algorithm for solvingthe job-shop problem[J]. Management Science,1989,35(2):164–176.
[187] Nyhuis, P. and Wiendahl, H P. Logistische kennlinien: Grundlagenwerkzeuge undanwendugen,2003,2nd edition. Berlin: Springer.(German)
[188] Matthias Hiisig. Online Workload Control with Shifting Bottlenecksand Due DateConstraints[C]. Proceedings of2009IEEE International Symposium on Assembly andManufacturing,2009, Suwon, Korea:17-20.
[189] Kim, S., Davis, K.R. and Cox III, J.F. III,2003. An investigation of output flow control,bottleneckflow control and dynamic flow control mechanisms in various simple linescenarios[J].Production Planning&Control,14(1):15–32.
[190] Nabil Mikatia. Dependence of lead time on batch size studied by a system dynamics model[J].International Journal of Production Research,2010,48(18):5523–5532.
[191]陈淮莉，张洁，马登哲.一种新型供应链物料需求计划模型[J].上海交通大学学报，2005,39(1):98-104.
[192] Zijm W H M, Buitenhek R. Capacity planning and lead time management[J]. InternationalJournal of Production Economics,1996,46-47(1):165-179
[193] Koo Pyung-Hoi, Lee Woon-Seek, Kim Youngjin. Production batch sizing at multi-productbottleneck machines[C]//2009International Conference on Computers and Industrial Engineering,Troyes, France,6-9July,2009:814-818.
[194] Matuszek J, Mleczko J. Production control in moving bottlenecks in conditions of unit andsmall-batch production[J]. Bulletin of the Polish Academy of Sciences: Technical Sciences,2009,57(3):229-239.
[195] Kooa Pyung-Hoi, Bulfinb Robert, Koh Shie-Gheun. Determination of batch size at abottleneck machine in manufacturing systems[J]. International Journal of Production Research,2007,45(5):1215–1231.
[196]华兴（美）.排队论与随机服务系统[M].上海：上海翻译出版公司，1987.
[197]焦李成,尚荣华,马文萍等.多目标优化免疫算法、理论和应用[M].北京：科学出版社.2010.
[198] Coello C A, Pulido G T, Lechuga M S. Handing multiple objectives with particle swarmoptimization. IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[199] Deb K, Pratap A. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II [J].IEEETransactions on Evolutionary Computation,2002,6(2):182-197.
[200] Deb K, Goldberg D E. An investigation of niche and species formation in genetic functionoptimization [C]. In the Third International Conference on Genetic Algorithms, Piscataway, NJ,1989:42-50.
[201]李晋航.混流制造车间物料配送调度优化研究[D].武汉：华中科技大学,2009.
[202] Kolen A, Rinnoy K A, Trienkens H. Vehicle Routing With Time Windows[J]. OperationResearch,1987,35:266-273.
[203]王文义,任刚.多种群退火贪婪混合遗传算法[J].计算机工程与应用,2005,23:60-62.
[204]孟佳娜,王立宏.具有自识别能力的遗传算法求解旅行商问题[J].计算机工程与应用,2006,13:51-53.
[205] Wu S.D., Storer R.N., Chang P. One-machine rescheduling heuristics with efficiency andstability as criteria[J]. Computers and Operations Research,1993,20(1):1-14.
[206] Wu S.D., Storer R.N., Chang P. One-machine rescheduling heuristics with efficiency andstability as criteria[J]. Computers and Operations Research,1993,20(1):1-14.
[207] Goren S, Sabuncuoglu I. Optimization of schedule robustness and stability under randommachine breakdowns and processing time variability[J]. IIE Transaction,2010,42:203-220.
[208] Leus R, Herroelen W. Scheduling for stability in single-machine production systems[J].Journal of Scheduling,2007,10:223-235.
[209] Van De Vonder S, Demeulemeester E, Herroelen W, et al. The trade-off between stability andmakespan in resource-constrained project scheduling[J]. International Journal of ProductionResearch,2006,44(2):215-236.
[210] Kouveilis P, Daniels R L, Vairaktarakis G. Robust scheduling of a two-machine two-shopwith uncertain processing times[J]. IIE Transactions,2000,32:421-432.
[211] Briskorn D, Leung J. Pinedo M. Robust scheduling on a single machine using time buffers[J].IIE Transaction,2011,43:383-398.
[212] Lu C, Lin S, Ying K. Robust scheduling on a single machine to minimize total flow time[J].Computers and Operation Research,2012,39:1682-1691.
[213] Goren S, Sabuncuoglu I. Robustness and stability measures for scheduling: single-machineenvironment[J]. IIE Transactions,2008,40:66-83.
[214] Du C, Pinedo M. A note on minimizing the expected makespan in flow shops subject tobreakdowns[J]. Naval Research Logistics,1995,42:1251-1262.
[215] Allahverdi A, Mittenthal J. Scheduling on a two-machine owshop subject to randombreakdowns with a makespan objective function[J]. European Journal of Operational Research,1995,81:376-387.
[216] Brige J, Glazebrrok K. Assessing the effects of machine breakdown in stochasticscheduling[J]. Operations Research Letters,1988,7:267-271.
[217] Yang J, Yu G. On the robust single machine scheduling problem[J]. Journal of CombinationOptimization,2002,6:17-33.
[218] Allahverdia A, Aydilek H. Heuristics for the two-machine flow shop scheduling problem tominimize makespan with bounded processing times[J]. International Journal of ProductionResearch,2010,48(21):6367-6385.
[219] Tao Jipin, Chao Zhijun, Xi Yugeng, et al. An optimal semi-online algorithm for a singlemachine scheduling problem with bounded processing time[J]. Information Processing Letters,2010,110(8-9):325-330.
[220] Kouvelis P, Yu G. Robust Discrete Optimization and Its Applications [M]. Boston: Kluwer,1997.
[221] Gentle, J. E. Random Number Generation and Monte Carlo Methods (2nd Edition)[M],2003,Springer.
[222] Leon V J, Wu S D, Storer R H. Robustness Measures and Robust Scheduling for JobShops[J]. IIE Transactions,1994,26(5):32-43.
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