钢铁企业散装原料场运行调度与优化问题研究
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摘要
钢铁工业是我国国民经济的重要支柱产业,其在快速发展的同时也面临着高成本、高能耗等诸多问题和挑战。以散装原料场为核心的原料贮运与处理工序,作为钢铁生产的第一步骤和钢铁企业供应链的重要环节,在降低原料成本、节能降耗方面扮演着重要角色。但一直以来,对钢铁企业散装原料场运行优化相关的研究都没有得到应有的重视,其运行管理的信息化与智能化在整个产业中也处于较低水平。因此,对钢铁企业散装原料场运行过程中的调度和优化问题进行研究,提高其生产效率和管理质量,不但可以填补相关研究领域的空白,对解决我国钢铁工业面临的问题也有着重要的现实意义。
本文在综述相关组合优化问题基本理论和研究现状、介绍分析钢铁工业散装原料场生产运行流程的基础上,对原料场运行过程中的几个主要问题进行抽象、定义,将其转化为特定类型的组合优化问题,建立数学模型,并设计了相应的求解算法。并且,通过将以上模型、算法与先进的定位与监控技术相结合,设计了一种钢铁企业原料场运行监控与优化系统,并在实际企业实现了应用。本文的主要工作和创新点如下：
(1)提出了一个描述钢铁企业散装原料场在有限生产周期内的存储空间分配问题的优化模型。该模型通过将原料出/入库过程离散化、对场地空间和原料料堆进行分块,以及建立一个复现原料场调度人员决策过程的双层决策框架,将原料场存储空间分配过程转化为一个多目标组合优化问题。作为问题的优化目标,该模型还对散装原料场混料风险以及场地空间利用率的量化指标进行了定义。
(2)提出了一种适用于求解原料场存储空间分配问题的Pareto多目标蚁群优化算法DCACO。该算法中,对应于问题目标函数的多个种群使用各自独立的信息素更新策略,在一个分散/集中框架引导下进行寻优,有效地改善了所求解集的收敛程度和在目标空间的分布情况；另外,该算法采用多信息素轨迹矩阵构架,独立记录每个生产周期内每个种群的信息素轨迹,使搜索过程有更好的指向性,提高了寻优效率。
(3)提出了一个描述面向炼铁原料混匀过程中原料场堆取料机调度问题的离散调度模型。该模型通过将原料场中的物料流抽象为“零件”和“产品”,并对原料场场地以及堆取料机移动路径进行划分,使炼铁原料混匀过程中的各种事件同步于一个离散时间轴上,从而将该过程转化成为一个带有有限中间存储、顺序相关准备时间的二阶组装流水车间调度问题(F2|STsd,block,assembly| Cmax)。在此基础上,考虑多堆取料机协同操作的情况,并在模型中加入符合实际工业过程的约束,将该问题拓展为一个带有有限中间存储、顺序相关准备时间、组装过程以及机器资格约束的二阶混合流水车间调度问题(HF2|STsd,Mj,block, assembly|Cmax)。
(4)提出了两种适用于求解单机条件下的炼铁原料混匀过程堆取料机调度问题的元启发式方法——包含种子染色体和“批次合并”算子的改进遗传算法iGA,以及带有禁忌定时器的改进蚁群算法ACOtt。这两种算法分别从各自角度,在传统遗传算法/蚁群算法基础上加入基于问题特性和知识的局部搜索/启发式过程,有效提高了其在求解该种类型调度问题时的性能。
(5)提出了一种适用于求解多机条件下的炼铁原料混匀过程堆取料机调度问题的蚁群算法ACO_AHFLB。该算法将蚁群中单个蚂蚁的寻优路径分解为多条子路径,分别用于问题中每台并行机上生产序列寻优,并使用多个信息素轨迹矩阵,对每条子路径上的信息素轨迹进行单独记录,提高了搜索效率；同时设计了一个子路径切换机制,使各条子路径在寻优过程中的时间轴尽可能保持同步,从而减少死锁现象的发生,提高了求解质量。
(6)设计了一个钢铁企业原料场实时监控与运行优化系统BMYRMOOS,并在实际钢铁企业实现了应用。该系统通过将本文所研究的原料场运行调度与优化问题的数学模型及其求解算法与差分定位、三维显示等技术相结合,实现了对钢铁企业散装原料场储空间分配情况和堆取料机工作状态的动态监控,并且根据料场生产计划与运行状态,为管理人员提供存储空间分配和堆取料机调度的优化决策方案,有效提高了钢铁企业原料场的管理质量和运行效率。
Iron and steel industry is an important pillar industry of the national economy. Along with the rapid development, it is also facing issues such as high material cost and high energy consumption. As the first step of the iron and steel production line, the bulk material yard which is responsible for the stroage and processing of raw material, plays an important role in cost reduction and energy conservation. However, for a long time, researches that are about the scheduling and optimization problems in bulk material yard are rarely found in the literatures. On the other hand, comparing to other procedure in the iron and steel production, the bulk material yard operation lacks for effective means of optimization. Hence, research on the operation scheduling and optimizaiton problems in bulk material yard of iron and steel industry not only expands the research area of the industrial operations, but also has important practical significance.
Based on the a review of the related researches on the combinatorial optimization problems and an introcution of the operation process of the bulk material yard, this thesis focus on some main scheduling and optimizaiton problems in the bulk materal yard operation and converts them into different types of combinatorial optimization problem. The corresponding mathimatical models and algorithms are also proposed. And by combining these models and algorithms with the technologies of positioning and monitoring, an real-time monitoring and operation optimization for bulk marterial yard is also designed and implemented in a real iron and steel plant. The main contributions of this thesis are presented as follows:
(1) A model for the optimization of bulk material yard storage space allocation in a finite horizon is proposed. In the model, the process in real industry is convert into a multi-objective combinatorial optimization problem via discretizing the inbound/outbound operations and partitioning the yard space and material piles, while a 2-layer decision making framework is also established to recreate the decision making process of the materail yard dispathcer. Moreover, the quantified measures for the material blending risk and the space utilization of the yard, which are the two main optimization objectives of the storage space allocation, are also defined in the model.
(2) A Pareto multi-objective ant colony optimization algorithm for the storage space allocation optimization, DCACO, is proposed. In the algorithm, two separated colonies are generated to search for each single objective, under indepent pheromone update rules and a distributed/centralized framework, which improves the convergence and the spread extent of the solution set. The multi-pheromone trail matrix strategy is also implemented in the algorithm, which gives the colonies more accurate guidance for search, and improves the computational efficiency.
(3) A model that describes the bucket-wheel excavator scheduling in the bulk ore blending process of iron making is proposed. By discretizing the time horizon and partitioning the yard space, eventually the bulk ore blending process is reformulated as a two stage assembly flow shop with sequence-depended setup time and limited intermediate buffer (F2|STsd, block, assembly| Cmax). Further more, the senario of muliple excavator cooperation and some other constraints in real industry are also considerd. And the model is generalized as a two stage hybrid flow shop with sequence-depended setup time, limited intermediate buffer, eligibility constraint and assembly process (HF2| STsd, Mj, block, assembly| Cmax).
(4) An improved genetic algorihm iGA and an improved ant colony optimization algorithm ACOtt for solving the single-excavator scheduling in bulk ore blending process are proposed,. Either algorithm is combined with problem knowledge-based local search procedure or heuristic, which improves the both the search speed and the optimal solutions'quality.
(5) An ant colony optimization algorithm ACO_AHFLB for solving the multi-excavator scheduling in bulk ore blending process is proposed. In the algorithm, the searching path of each ant is consist of mutiple sub-pathes, for searching the optimal schedulings on the parallel machines. Multiple pheromone matrices are used to improve the search effeciency. And a sub-path switch mechanism is also designed to synchronize the time horizons of all the sub-paths, in order to reduce the probability of deadlock during the solution construction.
(6) A real-time monitoring & operation optimization system for bulk material yard (BMYRMOOS) is designed and implentmented in a real iron and steel plant. By combining the mathmatical models and algorithms proposed in this thesis and technologies such as the differential global positioning and 3-D graphic display, the system is capable to realize the real-time monitoring of the storage space allocation and the excavators'status, and provide optimal decision support for the operations management on the bulk material yard of iron and steel industry.
本文在综述相关组合优化问题基本理论和研究现状、介绍分析钢铁工业散装原料场生产运行流程的基础上,对原料场运行过程中的几个主要问题进行抽象、定义,将其转化为特定类型的组合优化问题,建立数学模型,并设计了相应的求解算法。并且,通过将以上模型、算法与先进的定位与监控技术相结合,设计了一种钢铁企业原料场运行监控与优化系统,并在实际企业实现了应用。本文的主要工作和创新点如下：
(1)提出了一个描述钢铁企业散装原料场在有限生产周期内的存储空间分配问题的优化模型。该模型通过将原料出/入库过程离散化、对场地空间和原料料堆进行分块,以及建立一个复现原料场调度人员决策过程的双层决策框架,将原料场存储空间分配过程转化为一个多目标组合优化问题。作为问题的优化目标,该模型还对散装原料场混料风险以及场地空间利用率的量化指标进行了定义。
(2)提出了一种适用于求解原料场存储空间分配问题的Pareto多目标蚁群优化算法DCACO。该算法中,对应于问题目标函数的多个种群使用各自独立的信息素更新策略,在一个分散/集中框架引导下进行寻优,有效地改善了所求解集的收敛程度和在目标空间的分布情况；另外,该算法采用多信息素轨迹矩阵构架,独立记录每个生产周期内每个种群的信息素轨迹,使搜索过程有更好的指向性,提高了寻优效率。
(3)提出了一个描述面向炼铁原料混匀过程中原料场堆取料机调度问题的离散调度模型。该模型通过将原料场中的物料流抽象为“零件”和“产品”,并对原料场场地以及堆取料机移动路径进行划分,使炼铁原料混匀过程中的各种事件同步于一个离散时间轴上,从而将该过程转化成为一个带有有限中间存储、顺序相关准备时间的二阶组装流水车间调度问题(F2|STsd,block,assembly| Cmax)。在此基础上,考虑多堆取料机协同操作的情况,并在模型中加入符合实际工业过程的约束,将该问题拓展为一个带有有限中间存储、顺序相关准备时间、组装过程以及机器资格约束的二阶混合流水车间调度问题(HF2|STsd,Mj,block, assembly|Cmax)。
(4)提出了两种适用于求解单机条件下的炼铁原料混匀过程堆取料机调度问题的元启发式方法——包含种子染色体和“批次合并”算子的改进遗传算法iGA,以及带有禁忌定时器的改进蚁群算法ACOtt。这两种算法分别从各自角度,在传统遗传算法/蚁群算法基础上加入基于问题特性和知识的局部搜索/启发式过程,有效提高了其在求解该种类型调度问题时的性能。
(5)提出了一种适用于求解多机条件下的炼铁原料混匀过程堆取料机调度问题的蚁群算法ACO_AHFLB。该算法将蚁群中单个蚂蚁的寻优路径分解为多条子路径,分别用于问题中每台并行机上生产序列寻优,并使用多个信息素轨迹矩阵,对每条子路径上的信息素轨迹进行单独记录,提高了搜索效率；同时设计了一个子路径切换机制,使各条子路径在寻优过程中的时间轴尽可能保持同步,从而减少死锁现象的发生,提高了求解质量。
(6)设计了一个钢铁企业原料场实时监控与运行优化系统BMYRMOOS,并在实际钢铁企业实现了应用。该系统通过将本文所研究的原料场运行调度与优化问题的数学模型及其求解算法与差分定位、三维显示等技术相结合,实现了对钢铁企业散装原料场储空间分配情况和堆取料机工作状态的动态监控,并且根据料场生产计划与运行状态,为管理人员提供存储空间分配和堆取料机调度的优化决策方案,有效提高了钢铁企业原料场的管理质量和运行效率。
Iron and steel industry is an important pillar industry of the national economy. Along with the rapid development, it is also facing issues such as high material cost and high energy consumption. As the first step of the iron and steel production line, the bulk material yard which is responsible for the stroage and processing of raw material, plays an important role in cost reduction and energy conservation. However, for a long time, researches that are about the scheduling and optimization problems in bulk material yard are rarely found in the literatures. On the other hand, comparing to other procedure in the iron and steel production, the bulk material yard operation lacks for effective means of optimization. Hence, research on the operation scheduling and optimizaiton problems in bulk material yard of iron and steel industry not only expands the research area of the industrial operations, but also has important practical significance.
Based on the a review of the related researches on the combinatorial optimization problems and an introcution of the operation process of the bulk material yard, this thesis focus on some main scheduling and optimizaiton problems in the bulk materal yard operation and converts them into different types of combinatorial optimization problem. The corresponding mathimatical models and algorithms are also proposed. And by combining these models and algorithms with the technologies of positioning and monitoring, an real-time monitoring and operation optimization for bulk marterial yard is also designed and implemented in a real iron and steel plant. The main contributions of this thesis are presented as follows:
(1) A model for the optimization of bulk material yard storage space allocation in a finite horizon is proposed. In the model, the process in real industry is convert into a multi-objective combinatorial optimization problem via discretizing the inbound/outbound operations and partitioning the yard space and material piles, while a 2-layer decision making framework is also established to recreate the decision making process of the materail yard dispathcer. Moreover, the quantified measures for the material blending risk and the space utilization of the yard, which are the two main optimization objectives of the storage space allocation, are also defined in the model.
(2) A Pareto multi-objective ant colony optimization algorithm for the storage space allocation optimization, DCACO, is proposed. In the algorithm, two separated colonies are generated to search for each single objective, under indepent pheromone update rules and a distributed/centralized framework, which improves the convergence and the spread extent of the solution set. The multi-pheromone trail matrix strategy is also implemented in the algorithm, which gives the colonies more accurate guidance for search, and improves the computational efficiency.
(3) A model that describes the bucket-wheel excavator scheduling in the bulk ore blending process of iron making is proposed. By discretizing the time horizon and partitioning the yard space, eventually the bulk ore blending process is reformulated as a two stage assembly flow shop with sequence-depended setup time and limited intermediate buffer (F2|STsd, block, assembly| Cmax). Further more, the senario of muliple excavator cooperation and some other constraints in real industry are also considerd. And the model is generalized as a two stage hybrid flow shop with sequence-depended setup time, limited intermediate buffer, eligibility constraint and assembly process (HF2| STsd, Mj, block, assembly| Cmax).
(4) An improved genetic algorihm iGA and an improved ant colony optimization algorithm ACOtt for solving the single-excavator scheduling in bulk ore blending process are proposed,. Either algorithm is combined with problem knowledge-based local search procedure or heuristic, which improves the both the search speed and the optimal solutions'quality.
(5) An ant colony optimization algorithm ACO_AHFLB for solving the multi-excavator scheduling in bulk ore blending process is proposed. In the algorithm, the searching path of each ant is consist of mutiple sub-pathes, for searching the optimal schedulings on the parallel machines. Multiple pheromone matrices are used to improve the search effeciency. And a sub-path switch mechanism is also designed to synchronize the time horizons of all the sub-paths, in order to reduce the probability of deadlock during the solution construction.
(6) A real-time monitoring & operation optimization system for bulk material yard (BMYRMOOS) is designed and implentmented in a real iron and steel plant. By combining the mathmatical models and algorithms proposed in this thesis and technologies such as the differential global positioning and 3-D graphic display, the system is capable to realize the real-time monitoring of the storage space allocation and the excavators'status, and provide optimal decision support for the operations management on the bulk material yard of iron and steel industry.
引文
[1]中国产业信息网.2010年1-12月中国钢材产量分省市统计[J/OL] 2011,[2011.2.24]. http://data.chyxx.com/nykch/201102/J61043IO52.html.
[2]郭利杰.钢铁工业发展周期及中国钢产量饱和点预测[J].科技和产业,2011,3)：5-8.
[3]刘耀东.我国钢铁工业的现状、问题和调整对策[J].数据2011,(8)：59-61.
[4]路透社.铁矿石价格日益成为亚洲工业需求衡量指标[J/OL]2011,[2011.8.25].http://cn.reuters.com/article/CNAnalysesNews/idCNCHINA-4855320110901.
[5]贡艳荣.钢铁企业原材料成本控制模型的研究[D].大连；大连理工大学,2008.
[6]徐匡迪.低碳经济与钢铁工业[J].钢铁,2010,45(3)：1-12.
[7]李士琦,吴龙,纪志军,et al.中国钢铁工业节能减排现状及对策[J].钢铁研究,2011,39(3)：1-5.
[8]齐翼龙,王华,卿山,et al.我国钢铁工业高能耗原因及节能措施[J].冶金能源,2009,28(5-7)：
[9]中新社.国际气候谈判进展缓慢中国坚持兑现原先承诺[J/OL]2010,[2011.12.6]. http://news.cntv.cn/20101206/110324.shtml.
[10]柴天佑,金以慧,任德祥,et a1.基于三层结构的流程工业现代集成制造系统[J].控制工程,2002,9(3)：1-6.
[11]漆永新.我国钢铁工业信息化与自动化发展评述[J].冶金管理,2005,(4)：46-8.
[12]马竹梧.我国钢铁工业自动化技术应用60年的进展、问题与对策[J].自动化博览,2010,(1)：27-33.
[13]Tang L X, Liu J Y, Rong A Y, et al. A review of planning and scheduling systems and methods for integrated steel production [J]. Eur J Oper Res,2001, 133(1):1-20.
[14]李韶华,唐立新.大型钢铁企业原料场存储分配问题的研究[J].控制与决策,2006,21(6)：656-65.
[15]柯畅,郑永前.钢铁料场输入配置数学模型的研究[J].物流技术,2007,26(9)：73-6.
[16]尤艺,周立新,孙焰.宝钢矿石料场库位分配优化数模研究[J].物流科技,2009,(11)：
[17]Suh M S, Lee Y J, Ko Y K. A two-level hierarchical approach for raw material scheduling in steelworks [J]. Engineering Applications of Artificial Intelligence, 1997,10(5):503-15.
[18]Blum C, Roli A. Metaheuristics in Combinatorial Optimization:Overview and Conceptual Comparison [J]. ACM Computing Surveys,2003,35(3):268-308.
[19]Papadimitriou C H, Steiglitz K. Combinatorial optimization:algorithms and complexity [M]. New York:Dover Publications, Inc.,1982.
[20]Michael R. Garey D S J. Computers and intractability:a guide to the theory of NP-completeness [M]. W. H. Freeman & Co Ltd,1979.
[21]Osman I H, Laporte G. Metaheuristics:A bibliography [J]. Ann Oper Res,1996, 63(513-623.
[22]Holland J H. Adaptation in natural and artificial systems:An introductory analysis with applications to biology, control, and artificial intelligence [M]. Ann Arbor:University of Michigan Press,1975.
[23]王文杰,叶世伟.人工智能原理与应用[M].人民邮电出版社,2004.
[24]Dorigo M, Maniezzo V, Colorni A. The Ant System:An autocatalytic Optimizing Process [M]. Technical Report 91-016. Milan; Department of Electronics, Politercnico di Milano.1991:34-60.
[25]Dorigo M, Maniezzo V, Colorni A. Ant system:Optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,1996,26(1):29-41.
[26]钟一文.智能优化方法及其应用研究[D].杭州；浙江大学,2005.
[27]王笑蓉.蚁群优化的理论模型及在生产调度中的应用研究[D].杭州；浙江大学,2003.
[28]Aytug H, Khouja M, Vergara F E. Use of genetic algorithms to solve production and operations management problems:A review [J]. International Journal of Production Research,2003,41(17):3955-4009.
[29]Chaudhry S S, Luo W. Application of genetic algorithms in production and operations management:A review [J]. International Journal of Production Research,2005,43(19):4083-101.
[30]Dorigo M, Blum C. Ant colony optimization theory:A survey [J]. Theoretical Computer Science,2005,344(2-3):243-78.
[31]Wolpert D H, Macready W G. No free lunch theorems for optimization [J]. IEEE Transactions on Evolutionary Computation,1997,1(1):67-82.
[32]Caserta M, Voβ S. Metaheuristics:Intelligent Problem Solving Matheuristics [M]//MANIEZZO V, STTZLE T, VO S. Springer US.2010:1-38.
[33]Veldhuizen D A v, Lamont G B. Multiobjective evolutionary algorithm test suites [M]. Proceedings of the 1999 ACM symposium on Applied computing. San Antonio, Texas, United States; ACM.1999:351-7.
[34]Jaszkiewicz A. Multiple objective metaheuristic algorithms for combinatorial optimization [D]. Poznan; Poznan University of Technology,2001.
[35]郭秀萍.多目标进化算法及其在制造系统中的应用研究[D].上海；上海交通大学,2007.
[36]Fonseca C M, Fleming P J. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization; proceedings of the 5th International Conference on Genetic Algorithms California, USA, F,1993 [C]. Morgan Kaufmann Publishers Inc.
[37]Horn J, Nafpliotis N, Goldberg D E. A niched Pareto genetic algorithm for multiobjective optimization; proceedings of the Evolutionary Computation, 1994 IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference on, F 27-29 Jun 1994,1994 [C].
[38]Srinivas N, Deb K. Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms [J]. Evolutionary Computation,1994,2(3):221-48.
[39]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II [J]. IEEE Transactions on Evolutionary Computation,2002, 6(2):182-97.
[40]Dorigo M, Gambardella L M. Ant-Q:A Reinforcement Learning Approach to Combinatorial Optimization [M]. Tech Rep IRIDIA/95-01. Universite Libre de Bruxelles, Belgium.1995.
[41]C. E. Mariano, Morales E. A Multiple Objective Ant-Q Algorithm for the Design of Water Distribution Irrigation Networks; proceedings of the First International Workshop on Ant Colony Optimization, Brussels, Belgium, F, 1998 [C].
[42]Gambardella L M, Taillard E, Agazzi G. MACS-VRPTW:A MULTIPLE ANT COLONY SYSTEM FOR VEHICLE ROUTING PROBLEMS WITH TIME WINDOWS, F,1999 [C].
[43]Iredi S, Merkle D, Middendorf M. Bi-Criterion Optimization with Multi Colony Ant Algorithms [M]//ZITZLER E, THIELE L, DEB K, et al. Evolutionary Multi-Criterion Optimization. Springer Berlin/Heidelberg.2001:359-72.
[44]Garcia-Martinez C, Cordon O, Herrera F. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP [J]. Eur J Oper Res,2007,180(1):116-48.
[45]Doerner K, Hartl R F, Reimann M. Are COMPETants more competent for problem solving? The case of full truckload transportation [J]. Central European Journal of Operations Research,2003,11 (2):115-41.
[46]Doerner K, Gutjahr W J, Hartl R F, et al. Pareto ant colony optimization:A metaheuristic approach to multiobjective portfolio selection [J]. Ann Oper Res, 2004,131(1-4):79-99.
[47]Doerner K F, Gutjahr W J, Hartl R F, et al. Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection [J]. Eur J Oper Res,2006,171(3):830-41.
[48]Gravel M, Price W L, Gagne C. Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic [J]. Eur J Oper Res,2002,143(1):218-29.
[49]Cardoso P, Jesus M, Marquez A. MONACO-multi-objective network optimisation based on an ACO; proceedings of the X Encuentros de Geometria Computacional,, Seville, Spain, F,2003 [C].
[50]Baran B, Schaerer M. A multiobjective ant colony system for vehicle routing problem with time windows, F,2003 [C].
[51]Pinto D, Baran B. Solving multiobjective multicast routing problem with a new ant colony optimization approach; proceedings of the the 3rd international IFIP/ACM Latin American conference on Networking, Cali, Columbia, F,2005 [C].ACM:1168120.
[52]Angus D, Woodward C. Multiple objective ant colony optimisation [J]. Swarm Intelligence,2009,3(1):69-85.
[53]池元成,蔡国飙.基于蚁群算法的多目标优化[J].计算机工程,2009,35(15)：168-72.
[54]丁力平,谭建荣,冯毅雄, et al基于Pareto蚁群算法的拆卸线平衡多目标优化[J].计算机集成制造系统,2010,15(7)：1406-13.
[55]Deb K. Multi-Objective Optimization Using Evolutionary Algorithms [M]. New York:John Wiley & Sons, Inc.,2001.
[56]Veldhuizen D A V. Multiobjective evolutionary algorithms:classifications, analyses, and new innovations [D]. Wright Patterson AFB; Air Force Institute of Technology,1999.
[57]Zitzler E, Thiele L. Multiobjective evolutionary algorithms:a comparative case study and the strength Pareto approach [J]. IEEE Transactions on Evolutionary Computation,1999,3(4):257-71.
[58]Leung Y W, Wang Y. U-Measure:A Quality Measure for Multiobjective Programming [J]. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans,2003,33(3):337-43.
[59]Lizarraga G L, Aguirre A H, Rionda S B. G-metric:An M-ary quality indicator for the evaluation of non-dominated sets, F,2008 [C].
[60]Zitzler E. Evolutionary Algorithms for Multiobjective Optimization:Methods and Applications [D]. Zurich, Switzerland; Swiss Federal Institute of Technology (ETH),1999.
[61]Silva V L S, Wanner E F, Cerqueira S A A G, et al. A new performance metric for multiobjective optimization:The integrated sphere counting, F,2007 [C].
[62]Hansen M P, Jaszkiewicz A. Evaluating the quality of approximations to the non-dominated set [M]. Technical Report IMM-REP-1998-7.1998.
[63]Pinedo M. Scheduling:Theory, Algorithms, and Systems [M].3rd ed.: Prentice-Hall,2008.
[64]Graham R L, Lawler E L, Lenstra J K, et al. Optimization and Approximation in Deterministic Sequencing and Scheduling:a Survey [M].1979.
[65]Allahverdi A, Ng C T, Cheng T C E, et al. A survey of scheduling problems with setup times or costs [J]. Eur J Oper Res,2008,187(3):985-1032.
[66]Papadimitriou C H, Kanellakis P C. Flowshop scheduling with limited temporary storage [J]. Journal of the ACM,1980,27(3):533-49.
[67]Leisten R. FLOWSHOP SEQUENCING PROBLEMS WITH LIMITED BUFFER STORAGE [J]. International Journal of Production Research,1990, 28(11):2085-100.
[68]Smutnicki C. A two-machine permutation flow shop scheduling problem with buffers [J]. OR Spektrum,1998,20(4):229-35.
[69]Nowicki E. The permutation flow shop with buffers:A tabu search approach [J]. Eur J Oper Res,1999,116(1):205-19.
[70]Brucker P, Heitmann S, Hurink J. Flow-shop problems with intermediate buffers [J]. OR Spectrum,2003,25(4):549-74.
[71]Pranzo M. Batch scheduling in a two-machine flow shop with limited buffer and sequence independent setup times and removal times [J]. Eur J Oper Res, 2004,153(3):581-92.
[72]Liu S Q, Kozan E. Scheduling a flow shop with combined buffer conditions [J]. Int J Prod Econ,2009,117(2):371-80.
[73]Yaurima V, Burtseva L, Tchernykh A. Hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints and limited buffers [J]. Comput Ind Eng,2009,56(4):1452-63.
[74]徐智,韩兵,席裕庚.一类带缓冲区的混合Flow shop生产过程的Makespan生产调度[J].上海交通大学学报,2000,34(12)：1667-71.
[75]徐智,席裕庚,韩兵.基于遗传算法的一类带缓冲区的混合生产调度[J].控制理论与应用,2001,18(5)：675-80.
[76]王凌,张亮.有限缓冲区流水线调度的多搜索模式遗传算法[J].计算机集成制造系统,2005,11(7)：1041-6.
[77]Lee C-Y, Cheng T C E, Lin B M T. Minimizing the makespan in the 3-machine assembly-type flowshop scheduling problem [J]. Management Science,1993, 39(5):616-25.
[78]Hariri A M A, Potts C N. A branch and bound algorithm for the two-stage assembly scheduling problem [J]. Eur J Oper Res,1997,103(3):547-56.
[79]Sung C S, Kim H A. A two-stage multiple-machine assembly scheduling problem for minimizing sum of completion times [J]. Int J Prod Econ,2008, 113(2):1038-48.
[80]Yokoyama M. Hybrid flow-shop scheduling with assembly operations [J]. Int J Prod Econ,2001,73(2):103-16.
[81]Sun X, Morizawa K, Nagasawa H. Powerful heuristics to minimize makespan in fixed,3-machine, assembly-type flowshop scheduling [J]. Eur J Oper Res, 2003,146(3):498-516.
[82]Tozkapan A, Kirca O, Chung C S. A branch and bound algorithm to minimize the total weighted flowtime for the two-stage assembly scheduling problem [J]. Comput Oper Res,2003,30(2):309-20.
[83]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]. Eur J Oper Res,2007,182(1):80-94.
[84]Allahverdi A, Al-Anzi F S. The two-stage assembly scheduling problem to minimize total completion time with setup times [J]. Comput Oper Res,2009, 36(10):2740-7.
[85]Al-Anzi F S, Allahverdi A. Heuristics for a two-stage assembly flowshop with bicriteria of maximum lateness and makespan [J]. Comput Oper Res,2009, 36(9):2682-9.
[86]Corwin B D, Esogbue A O. TWO MACHINE FLOW SHOP SCHEDULING PROBLEMS WITH SEQUENCE DEPENDENT SETUP TIMES:A DYNAMIC PROGRAMMING APPROACH [J]. Naval Research Logistics, 1974,21(3):515-24.
[87]Allahverdi A, Gupta J N D, Aldowaisan T. A review of scheduling research involving setup considerations [J]. Omega,1999,27(2):219-39.
[88]Cheng T C E, Gupta J N D, Wang G. A review of flowshop scheduling research with setup times [J]. Production and Operations Management,2000,9(3): 262-82.
[89]S A T, G R K. An Extension of Two Machines Sequencing Problem [J]. Opsearch,1971,8(1):
[90]Ruiz R, Vazquez-Rodriguez J A. The hybrid flow shop scheduling problem [J]. Eur J Oper Res,2010,205(1):1-18.
[91]Behnamian J, Zandieh M, Fatemi Ghomi S M T. Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm [J]. Expert Systems with Applications,2009,36(6):9637-44.
[92]Gupta J N D, Hariri A M A, Potts C N. Scheduling a two-stage hybrid flow shop with parallel machines at the first stage [J]. Ann Oper Res,1997,69(): 171-91.
[93]Voss S. The two-stage hybrid flow shop with sequence-dependent setup times; proceedings of the a Joint German/US Conference, F,1993 [C]. Springer-Verlag.
[94]Chen C L, Usher J M, Palanimuthu N. A tabu search based heuristic for a flexible flow line with minimum flow time criterion [J]. International Journal of Industrial Engineering-Theory Applications and Practice,1998,5(2):157-68.
[95]Xiao W, Hao P, Zhang S, et al. Hybrid flow shop scheduling using genetic algorithms; proceedings of the the Third World Congress on Intelligent Control and Automation, F,2000 [C]. IEEE Press.
[96]Oguz C, Ercan M. A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks [J]. Journal of Scheduling,2005,8(4):323-51.
[97]Alaykyran K, Engin O, Doyen A. Using ant colony optimization to solve hybrid flow shop scheduling problems [J]. Int J Adv Manuf Technol,2007,35(5-6): 541-50.
[98]王万良,姚明海,吴云高,et al基于遗传算法的混合Flow-shop调度方法[J].系统仿真学报,2002,14(7)：863-5.
[99]唐立新,吴亚萍.混合流水车间调度的遗传下降算法[J].自动化学报,2002,28(4)：637-41.
[100]Ib Finn P. Blending in circular and longitudinal mixing piles [J]. Chemometrics and Intelligent Laboratory Systems,2004,74(1):135-41.
[101]Fordertechnik T. Open Pit Mining, Mineral Processing, Materials Handling program [M/OL].2001. http://www.thyssenkrupp-foerdertechnik.de/.
[102]中国冶金建设协会.GB 50541-2009钢铁企业原料场工艺设计规范[S]2009
[103]王玉兴,唐艳芹,魏民祥,et al悬臂式斗轮堆取料机的发展及研究动向 [J].建筑机械,1999,(8)：25-9.
[104]Techint. Bulk ore blending in yards [M/OL]. Material Handling Systems,1999. http://www.steelplantequipment.com/.
[105]王凌,王雄,金以慧.MES——流程工业CIMS发展的关键[J].化工自动化及仪表,2001,28(4)：1-5.
[106]Zhang C, Liu J, Wan Y-w, et al. Storage space allocation in container terminals [J]. Transportation Research Part B:Methodological,2003,37(10):883-903.
[107]Bazzazi M, Safaei N, Javadian N. A genetic algorithm to solve the storage space allocation problem in a container terminal [J]. Comput Ind Eng,2009,56(1): 44-52.
[108]Chaharsooghi S K, Meimand Kermani A H. An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP) [J]. Applied Mathematics and Computation,2008,200(1):167-77.
[109]Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms:empirical results [J]. Evolutionary Computation,2000,8(2): 173-95.
[110]Dorigo M, Gambardella L M. Ant colony system:a cooperative learning approach to the traveling salesman problem [J]. Evolutionary Computation, IEEE Transactions on,1997,1(1):53-66.
[111]Sara Hatami S E, Reza Tavakkoli-Moghaddam and Yasaman Maboudian. Two meta-heuristics for three-stage assembly flowshop scheduling with sequence-dependent setup times [J]. The International Journal of Advanced Manufacturing Technology,2010,50(9-12):1153-64.
[112]Zbigniew M. Genetic Algorithms+Data Structures=Evolution Programs [M]. 3rd ed.:Springer-Verlag, Berlin.,1996.
[113]Reeves C R. A GENETIC ALGORITHM FOR FLOWSHOP SEQUENCING [J]. Comput Oper Res,1995,22(1):5-13.
[114]Murata T, Ishibuchi H, Tanaka H. Genetic algorithms for flowshop scheduling problems [J]. Computers and Industrial Engineering,1996,30(4):1061-71.
[115]詹士昌,徐婕,吴俊.蚁群算法中有关算法参数的最优选择[J].科技通报,2003,19(5)：381-6.
[116]Neron E, Baptiste P, Gupta J N D. Solving hybrid flow shop problem using energetic reasoning and global operations [J]. Omega,2001,29(6):501-11.
[117]Logendran R, Salmasi N, Sriskandarajah C. Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups [J]. Computers and Operations Research,2006,33(1):158-80.
[118]Takekoshi A, Inaba M, Satoh Y, et al. Raw material yard expert system at Fukuyama works [J]. NKK Technical Review,1991, (61):1-9.
[119]沈中卫,黄新庭.钢铁企业散装料场问题分析及解决方案[J].冶金管理,2006,1(1)：49-52.
[120]朱银华,胡乃联,王洪松,et al.原料场库存量智能管理系统的开发；proceedings of the中国计量协会冶金分会2007年会,F,2007[C].
[121]王璟,许志梅,王艳平.韶钢原料场综合管理系统的应用[J].国内外机电一体化,2010,(3)：54-6.
[122]杨刚.原料场库存管理系统的开发及应用proceedings of the全国冶金自动化信息网2011年年会,F,201][C].
[123]Coelho R J, Lana P F, Silva A C, et al. Operational simulation model of the raw material handling in an integrated steel making plant, F,2009 [C].
[124]张卫,王国华.钢铁企业大型料场取料机激光测距定位系统[J].物流技术,2006,1(1)：
[125]吴歧峻,沈中卫.LD-1激光扫描自动测量系统[J].工业计量,2005,1(S1)：136-8.
[126]胡玉良,吉同祥,张逸国,et al.散货料场无人化堆取工艺：中国,200610028966 [P/OL].2011-1-19[2011-09-06].
[127]Ergen E, Akinci B, Sacks R. Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS [J]. Automation in Construction,2007,16(3):354-67.
[128]冯林刚,张宗海.关于GPS控制网WGS84平差坐标向地方独立坐标系的转换[J].测绘通报,2005,3(3)：27-9.
[129]刘宗泉,贾志强,邢诚, et al. GPS网WGS-84平差坐标向地方独立坐标的转换[J].测绘信息与工程,2007,32(1)：33-5.
[2]郭利杰.钢铁工业发展周期及中国钢产量饱和点预测[J].科技和产业,2011,3)：5-8.
[3]刘耀东.我国钢铁工业的现状、问题和调整对策[J].数据2011,(8)：59-61.
[4]路透社.铁矿石价格日益成为亚洲工业需求衡量指标[J/OL]2011,[2011.8.25].http://cn.reuters.com/article/CNAnalysesNews/idCNCHINA-4855320110901.
[5]贡艳荣.钢铁企业原材料成本控制模型的研究[D].大连；大连理工大学,2008.
[6]徐匡迪.低碳经济与钢铁工业[J].钢铁,2010,45(3)：1-12.
[7]李士琦,吴龙,纪志军,et al.中国钢铁工业节能减排现状及对策[J].钢铁研究,2011,39(3)：1-5.
[8]齐翼龙,王华,卿山,et al.我国钢铁工业高能耗原因及节能措施[J].冶金能源,2009,28(5-7)：
[9]中新社.国际气候谈判进展缓慢中国坚持兑现原先承诺[J/OL]2010,[2011.12.6]. http://news.cntv.cn/20101206/110324.shtml.
[10]柴天佑,金以慧,任德祥,et a1.基于三层结构的流程工业现代集成制造系统[J].控制工程,2002,9(3)：1-6.
[11]漆永新.我国钢铁工业信息化与自动化发展评述[J].冶金管理,2005,(4)：46-8.
[12]马竹梧.我国钢铁工业自动化技术应用60年的进展、问题与对策[J].自动化博览,2010,(1)：27-33.
[13]Tang L X, Liu J Y, Rong A Y, et al. A review of planning and scheduling systems and methods for integrated steel production [J]. Eur J Oper Res,2001, 133(1):1-20.
[14]李韶华,唐立新.大型钢铁企业原料场存储分配问题的研究[J].控制与决策,2006,21(6)：656-65.
[15]柯畅,郑永前.钢铁料场输入配置数学模型的研究[J].物流技术,2007,26(9)：73-6.
[16]尤艺,周立新,孙焰.宝钢矿石料场库位分配优化数模研究[J].物流科技,2009,(11)：
[17]Suh M S, Lee Y J, Ko Y K. A two-level hierarchical approach for raw material scheduling in steelworks [J]. Engineering Applications of Artificial Intelligence, 1997,10(5):503-15.
[18]Blum C, Roli A. Metaheuristics in Combinatorial Optimization:Overview and Conceptual Comparison [J]. ACM Computing Surveys,2003,35(3):268-308.
[19]Papadimitriou C H, Steiglitz K. Combinatorial optimization:algorithms and complexity [M]. New York:Dover Publications, Inc.,1982.
[20]Michael R. Garey D S J. Computers and intractability:a guide to the theory of NP-completeness [M]. W. H. Freeman & Co Ltd,1979.
[21]Osman I H, Laporte G. Metaheuristics:A bibliography [J]. Ann Oper Res,1996, 63(513-623.
[22]Holland J H. Adaptation in natural and artificial systems:An introductory analysis with applications to biology, control, and artificial intelligence [M]. Ann Arbor:University of Michigan Press,1975.
[23]王文杰,叶世伟.人工智能原理与应用[M].人民邮电出版社,2004.
[24]Dorigo M, Maniezzo V, Colorni A. The Ant System:An autocatalytic Optimizing Process [M]. Technical Report 91-016. Milan; Department of Electronics, Politercnico di Milano.1991:34-60.
[25]Dorigo M, Maniezzo V, Colorni A. Ant system:Optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,1996,26(1):29-41.
[26]钟一文.智能优化方法及其应用研究[D].杭州；浙江大学,2005.
[27]王笑蓉.蚁群优化的理论模型及在生产调度中的应用研究[D].杭州；浙江大学,2003.
[28]Aytug H, Khouja M, Vergara F E. Use of genetic algorithms to solve production and operations management problems:A review [J]. International Journal of Production Research,2003,41(17):3955-4009.
[29]Chaudhry S S, Luo W. Application of genetic algorithms in production and operations management:A review [J]. International Journal of Production Research,2005,43(19):4083-101.
[30]Dorigo M, Blum C. Ant colony optimization theory:A survey [J]. Theoretical Computer Science,2005,344(2-3):243-78.
[31]Wolpert D H, Macready W G. No free lunch theorems for optimization [J]. IEEE Transactions on Evolutionary Computation,1997,1(1):67-82.
[32]Caserta M, Voβ S. Metaheuristics:Intelligent Problem Solving Matheuristics [M]//MANIEZZO V, STTZLE T, VO S. Springer US.2010:1-38.
[33]Veldhuizen D A v, Lamont G B. Multiobjective evolutionary algorithm test suites [M]. Proceedings of the 1999 ACM symposium on Applied computing. San Antonio, Texas, United States; ACM.1999:351-7.
[34]Jaszkiewicz A. Multiple objective metaheuristic algorithms for combinatorial optimization [D]. Poznan; Poznan University of Technology,2001.
[35]郭秀萍.多目标进化算法及其在制造系统中的应用研究[D].上海；上海交通大学,2007.
[36]Fonseca C M, Fleming P J. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization; proceedings of the 5th International Conference on Genetic Algorithms California, USA, F,1993 [C]. Morgan Kaufmann Publishers Inc.
[37]Horn J, Nafpliotis N, Goldberg D E. A niched Pareto genetic algorithm for multiobjective optimization; proceedings of the Evolutionary Computation, 1994 IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference on, F 27-29 Jun 1994,1994 [C].
[38]Srinivas N, Deb K. Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms [J]. Evolutionary Computation,1994,2(3):221-48.
[39]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II [J]. IEEE Transactions on Evolutionary Computation,2002, 6(2):182-97.
[40]Dorigo M, Gambardella L M. Ant-Q:A Reinforcement Learning Approach to Combinatorial Optimization [M]. Tech Rep IRIDIA/95-01. Universite Libre de Bruxelles, Belgium.1995.
[41]C. E. Mariano, Morales E. A Multiple Objective Ant-Q Algorithm for the Design of Water Distribution Irrigation Networks; proceedings of the First International Workshop on Ant Colony Optimization, Brussels, Belgium, F, 1998 [C].
[42]Gambardella L M, Taillard E, Agazzi G. MACS-VRPTW:A MULTIPLE ANT COLONY SYSTEM FOR VEHICLE ROUTING PROBLEMS WITH TIME WINDOWS, F,1999 [C].
[43]Iredi S, Merkle D, Middendorf M. Bi-Criterion Optimization with Multi Colony Ant Algorithms [M]//ZITZLER E, THIELE L, DEB K, et al. Evolutionary Multi-Criterion Optimization. Springer Berlin/Heidelberg.2001:359-72.
[44]Garcia-Martinez C, Cordon O, Herrera F. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP [J]. Eur J Oper Res,2007,180(1):116-48.
[45]Doerner K, Hartl R F, Reimann M. Are COMPETants more competent for problem solving? The case of full truckload transportation [J]. Central European Journal of Operations Research,2003,11 (2):115-41.
[46]Doerner K, Gutjahr W J, Hartl R F, et al. Pareto ant colony optimization:A metaheuristic approach to multiobjective portfolio selection [J]. Ann Oper Res, 2004,131(1-4):79-99.
[47]Doerner K F, Gutjahr W J, Hartl R F, et al. Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection [J]. Eur J Oper Res,2006,171(3):830-41.
[48]Gravel M, Price W L, Gagne C. Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic [J]. Eur J Oper Res,2002,143(1):218-29.
[49]Cardoso P, Jesus M, Marquez A. MONACO-multi-objective network optimisation based on an ACO; proceedings of the X Encuentros de Geometria Computacional,, Seville, Spain, F,2003 [C].
[50]Baran B, Schaerer M. A multiobjective ant colony system for vehicle routing problem with time windows, F,2003 [C].
[51]Pinto D, Baran B. Solving multiobjective multicast routing problem with a new ant colony optimization approach; proceedings of the the 3rd international IFIP/ACM Latin American conference on Networking, Cali, Columbia, F,2005 [C].ACM:1168120.
[52]Angus D, Woodward C. Multiple objective ant colony optimisation [J]. Swarm Intelligence,2009,3(1):69-85.
[53]池元成,蔡国飙.基于蚁群算法的多目标优化[J].计算机工程,2009,35(15)：168-72.
[54]丁力平,谭建荣,冯毅雄, et al基于Pareto蚁群算法的拆卸线平衡多目标优化[J].计算机集成制造系统,2010,15(7)：1406-13.
[55]Deb K. Multi-Objective Optimization Using Evolutionary Algorithms [M]. New York:John Wiley & Sons, Inc.,2001.
[56]Veldhuizen D A V. Multiobjective evolutionary algorithms:classifications, analyses, and new innovations [D]. Wright Patterson AFB; Air Force Institute of Technology,1999.
[57]Zitzler E, Thiele L. Multiobjective evolutionary algorithms:a comparative case study and the strength Pareto approach [J]. IEEE Transactions on Evolutionary Computation,1999,3(4):257-71.
[58]Leung Y W, Wang Y. U-Measure:A Quality Measure for Multiobjective Programming [J]. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans,2003,33(3):337-43.
[59]Lizarraga G L, Aguirre A H, Rionda S B. G-metric:An M-ary quality indicator for the evaluation of non-dominated sets, F,2008 [C].
[60]Zitzler E. Evolutionary Algorithms for Multiobjective Optimization:Methods and Applications [D]. Zurich, Switzerland; Swiss Federal Institute of Technology (ETH),1999.
[61]Silva V L S, Wanner E F, Cerqueira S A A G, et al. A new performance metric for multiobjective optimization:The integrated sphere counting, F,2007 [C].
[62]Hansen M P, Jaszkiewicz A. Evaluating the quality of approximations to the non-dominated set [M]. Technical Report IMM-REP-1998-7.1998.
[63]Pinedo M. Scheduling:Theory, Algorithms, and Systems [M].3rd ed.: Prentice-Hall,2008.
[64]Graham R L, Lawler E L, Lenstra J K, et al. Optimization and Approximation in Deterministic Sequencing and Scheduling:a Survey [M].1979.
[65]Allahverdi A, Ng C T, Cheng T C E, et al. A survey of scheduling problems with setup times or costs [J]. Eur J Oper Res,2008,187(3):985-1032.
[66]Papadimitriou C H, Kanellakis P C. Flowshop scheduling with limited temporary storage [J]. Journal of the ACM,1980,27(3):533-49.
[67]Leisten R. FLOWSHOP SEQUENCING PROBLEMS WITH LIMITED BUFFER STORAGE [J]. International Journal of Production Research,1990, 28(11):2085-100.
[68]Smutnicki C. A two-machine permutation flow shop scheduling problem with buffers [J]. OR Spektrum,1998,20(4):229-35.
[69]Nowicki E. The permutation flow shop with buffers:A tabu search approach [J]. Eur J Oper Res,1999,116(1):205-19.
[70]Brucker P, Heitmann S, Hurink J. Flow-shop problems with intermediate buffers [J]. OR Spectrum,2003,25(4):549-74.
[71]Pranzo M. Batch scheduling in a two-machine flow shop with limited buffer and sequence independent setup times and removal times [J]. Eur J Oper Res, 2004,153(3):581-92.
[72]Liu S Q, Kozan E. Scheduling a flow shop with combined buffer conditions [J]. Int J Prod Econ,2009,117(2):371-80.
[73]Yaurima V, Burtseva L, Tchernykh A. Hybrid flowshop with unrelated machines, sequence-dependent setup time, availability constraints and limited buffers [J]. Comput Ind Eng,2009,56(4):1452-63.
[74]徐智,韩兵,席裕庚.一类带缓冲区的混合Flow shop生产过程的Makespan生产调度[J].上海交通大学学报,2000,34(12)：1667-71.
[75]徐智,席裕庚,韩兵.基于遗传算法的一类带缓冲区的混合生产调度[J].控制理论与应用,2001,18(5)：675-80.
[76]王凌,张亮.有限缓冲区流水线调度的多搜索模式遗传算法[J].计算机集成制造系统,2005,11(7)：1041-6.
[77]Lee C-Y, Cheng T C E, Lin B M T. Minimizing the makespan in the 3-machine assembly-type flowshop scheduling problem [J]. Management Science,1993, 39(5):616-25.
[78]Hariri A M A, Potts C N. A branch and bound algorithm for the two-stage assembly scheduling problem [J]. Eur J Oper Res,1997,103(3):547-56.
[79]Sung C S, Kim H A. A two-stage multiple-machine assembly scheduling problem for minimizing sum of completion times [J]. Int J Prod Econ,2008, 113(2):1038-48.
[80]Yokoyama M. Hybrid flow-shop scheduling with assembly operations [J]. Int J Prod Econ,2001,73(2):103-16.
[81]Sun X, Morizawa K, Nagasawa H. Powerful heuristics to minimize makespan in fixed,3-machine, assembly-type flowshop scheduling [J]. Eur J Oper Res, 2003,146(3):498-516.
[82]Tozkapan A, Kirca O, Chung C S. A branch and bound algorithm to minimize the total weighted flowtime for the two-stage assembly scheduling problem [J]. Comput Oper Res,2003,30(2):309-20.
[83]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]. Eur J Oper Res,2007,182(1):80-94.
[84]Allahverdi A, Al-Anzi F S. The two-stage assembly scheduling problem to minimize total completion time with setup times [J]. Comput Oper Res,2009, 36(10):2740-7.
[85]Al-Anzi F S, Allahverdi A. Heuristics for a two-stage assembly flowshop with bicriteria of maximum lateness and makespan [J]. Comput Oper Res,2009, 36(9):2682-9.
[86]Corwin B D, Esogbue A O. TWO MACHINE FLOW SHOP SCHEDULING PROBLEMS WITH SEQUENCE DEPENDENT SETUP TIMES:A DYNAMIC PROGRAMMING APPROACH [J]. Naval Research Logistics, 1974,21(3):515-24.
[87]Allahverdi A, Gupta J N D, Aldowaisan T. A review of scheduling research involving setup considerations [J]. Omega,1999,27(2):219-39.
[88]Cheng T C E, Gupta J N D, Wang G. A review of flowshop scheduling research with setup times [J]. Production and Operations Management,2000,9(3): 262-82.
[89]S A T, G R K. An Extension of Two Machines Sequencing Problem [J]. Opsearch,1971,8(1):
[90]Ruiz R, Vazquez-Rodriguez J A. The hybrid flow shop scheduling problem [J]. Eur J Oper Res,2010,205(1):1-18.
[91]Behnamian J, Zandieh M, Fatemi Ghomi S M T. Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm [J]. Expert Systems with Applications,2009,36(6):9637-44.
[92]Gupta J N D, Hariri A M A, Potts C N. Scheduling a two-stage hybrid flow shop with parallel machines at the first stage [J]. Ann Oper Res,1997,69(): 171-91.
[93]Voss S. The two-stage hybrid flow shop with sequence-dependent setup times; proceedings of the a Joint German/US Conference, F,1993 [C]. Springer-Verlag.
[94]Chen C L, Usher J M, Palanimuthu N. A tabu search based heuristic for a flexible flow line with minimum flow time criterion [J]. International Journal of Industrial Engineering-Theory Applications and Practice,1998,5(2):157-68.
[95]Xiao W, Hao P, Zhang S, et al. Hybrid flow shop scheduling using genetic algorithms; proceedings of the the Third World Congress on Intelligent Control and Automation, F,2000 [C]. IEEE Press.
[96]Oguz C, Ercan M. A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks [J]. Journal of Scheduling,2005,8(4):323-51.
[97]Alaykyran K, Engin O, Doyen A. Using ant colony optimization to solve hybrid flow shop scheduling problems [J]. Int J Adv Manuf Technol,2007,35(5-6): 541-50.
[98]王万良,姚明海,吴云高,et al基于遗传算法的混合Flow-shop调度方法[J].系统仿真学报,2002,14(7)：863-5.
[99]唐立新,吴亚萍.混合流水车间调度的遗传下降算法[J].自动化学报,2002,28(4)：637-41.
[100]Ib Finn P. Blending in circular and longitudinal mixing piles [J]. Chemometrics and Intelligent Laboratory Systems,2004,74(1):135-41.
[101]Fordertechnik T. Open Pit Mining, Mineral Processing, Materials Handling program [M/OL].2001. http://www.thyssenkrupp-foerdertechnik.de/.
[102]中国冶金建设协会.GB 50541-2009钢铁企业原料场工艺设计规范[S]2009
[103]王玉兴,唐艳芹,魏民祥,et al悬臂式斗轮堆取料机的发展及研究动向 [J].建筑机械,1999,(8)：25-9.
[104]Techint. Bulk ore blending in yards [M/OL]. Material Handling Systems,1999. http://www.steelplantequipment.com/.
[105]王凌,王雄,金以慧.MES——流程工业CIMS发展的关键[J].化工自动化及仪表,2001,28(4)：1-5.
[106]Zhang C, Liu J, Wan Y-w, et al. Storage space allocation in container terminals [J]. Transportation Research Part B:Methodological,2003,37(10):883-903.
[107]Bazzazi M, Safaei N, Javadian N. A genetic algorithm to solve the storage space allocation problem in a container terminal [J]. Comput Ind Eng,2009,56(1): 44-52.
[108]Chaharsooghi S K, Meimand Kermani A H. An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP) [J]. Applied Mathematics and Computation,2008,200(1):167-77.
[109]Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms:empirical results [J]. Evolutionary Computation,2000,8(2): 173-95.
[110]Dorigo M, Gambardella L M. Ant colony system:a cooperative learning approach to the traveling salesman problem [J]. Evolutionary Computation, IEEE Transactions on,1997,1(1):53-66.
[111]Sara Hatami S E, Reza Tavakkoli-Moghaddam and Yasaman Maboudian. Two meta-heuristics for three-stage assembly flowshop scheduling with sequence-dependent setup times [J]. The International Journal of Advanced Manufacturing Technology,2010,50(9-12):1153-64.
[112]Zbigniew M. Genetic Algorithms+Data Structures=Evolution Programs [M]. 3rd ed.:Springer-Verlag, Berlin.,1996.
[113]Reeves C R. A GENETIC ALGORITHM FOR FLOWSHOP SEQUENCING [J]. Comput Oper Res,1995,22(1):5-13.
[114]Murata T, Ishibuchi H, Tanaka H. Genetic algorithms for flowshop scheduling problems [J]. Computers and Industrial Engineering,1996,30(4):1061-71.
[115]詹士昌,徐婕,吴俊.蚁群算法中有关算法参数的最优选择[J].科技通报,2003,19(5)：381-6.
[116]Neron E, Baptiste P, Gupta J N D. Solving hybrid flow shop problem using energetic reasoning and global operations [J]. Omega,2001,29(6):501-11.
[117]Logendran R, Salmasi N, Sriskandarajah C. Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups [J]. Computers and Operations Research,2006,33(1):158-80.
[118]Takekoshi A, Inaba M, Satoh Y, et al. Raw material yard expert system at Fukuyama works [J]. NKK Technical Review,1991, (61):1-9.
[119]沈中卫,黄新庭.钢铁企业散装料场问题分析及解决方案[J].冶金管理,2006,1(1)：49-52.
[120]朱银华,胡乃联,王洪松,et al.原料场库存量智能管理系统的开发；proceedings of the中国计量协会冶金分会2007年会,F,2007[C].
[121]王璟,许志梅,王艳平.韶钢原料场综合管理系统的应用[J].国内外机电一体化,2010,(3)：54-6.
[122]杨刚.原料场库存管理系统的开发及应用proceedings of the全国冶金自动化信息网2011年年会,F,201][C].
[123]Coelho R J, Lana P F, Silva A C, et al. Operational simulation model of the raw material handling in an integrated steel making plant, F,2009 [C].
[124]张卫,王国华.钢铁企业大型料场取料机激光测距定位系统[J].物流技术,2006,1(1)：
[125]吴歧峻,沈中卫.LD-1激光扫描自动测量系统[J].工业计量,2005,1(S1)：136-8.
[126]胡玉良,吉同祥,张逸国,et al.散货料场无人化堆取工艺：中国,200610028966 [P/OL].2011-1-19[2011-09-06].
[127]Ergen E, Akinci B, Sacks R. Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS [J]. Automation in Construction,2007,16(3):354-67.
[128]冯林刚,张宗海.关于GPS控制网WGS84平差坐标向地方独立坐标系的转换[J].测绘通报,2005,3(3)：27-9.
[129]刘宗泉,贾志强,邢诚, et al. GPS网WGS-84平差坐标向地方独立坐标的转换[J].测绘信息与工程,2007,32(1)：33-5.