基于多Agent的钢铁生产复杂物流系统建模与仿真研究
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摘要
在当前资源严重短缺,环境严重恶化,环保政策日益严格的情况下,钢铁生产企业要实现节能减排目标,进一步提高自身适应性和国际竞争能力,实现资源循环利用和本身可持续性发展,必须对企业生产物流系统进行研究、分析和优化。
钢铁生产是一个多工序、多工位、空间跨度大、生产品种多的具有动态和不确定性的复杂物流系统。钢铁生产复杂物流系统是影响产品品种、质量和产量的关键因素,认识其生产物流规律,进行合理的生产计划及调度,是实现生产物流畅通的保障,是提高产品产量和降低生产成本的关键。
Agent的自主性、社会性、反应性、主动性、移动性、理智性等特性可以用来实现动态的、不确定环境的、大规模的软件系统。多Agent系统把多个Agent有效组织起来,相互协作和交流,形成问题的求解环境,并根据环境和交流知识进行推理、学习等,能够有效实现系统整体性能的提高和适应系统的灵活性、柔性、开放性等要求。面向Agent的开发方法已经成为软件工程领域的新趋势,为复杂系统的理解、建模、开发提供了一种很自然的方法,它使分布式的结构变得更简单、智能化和具有鲁棒性。
本文旨在利用多Agent技术探索一种既能描述钢铁生产过程复杂物流系统特性,又能反应物流系统的动态特征,并能对不同形式的钢铁生产流程具有广泛适应性的建模仿真方法和软件工程设计方法,在理论和实践方面均具有非常重要的意义。
针对钢铁生产过程灵活性、柔性和适应性的要求及其物流系统的复杂性特点,根据Agent技术优势及多Agent系统优点,其应用在钢铁生产复杂物流仿真系统中时可有效克服已有建模方法的不足,提出了基于多Agent的钢铁生产复杂物流仿真系统建模方法,把钢铁生产过程复杂的物流系统抽象为一个多Agent系统,基于通用性原则对仿真系统模型进行软件工程的分析、设计与实现技术研究。首先在对钢铁生产物流系统复杂性充分认识和把握的基础上,把多Agent技术引入到钢铁生产复杂物流系统的建模过程中,实现对基于多Agent的钢铁生产复杂物流仿真系统的系统功能分层抽象和定义；其次,通过分析仿真系统中Agent类型及结构,在对Petri网结构和功能进行扩展和Agent行为理论及Agent间交互行为理论拓展的基础上,建立了仿真系统中Agent的行为及其之间的交互模型,并借助形式化描述工具Petri网实现Agent内部动作和外部动作及其之间交互的形式化建模；另外,由于钢铁生产过程中物流系统灵活性和柔性的特殊要求,致使钢铁生产过程中的运输系统在整个物流系统中具有举足轻重的地位,本文就运输系统中天车运行机制进行详细分析和研究的基础上,实现了运输系统和物流仿真系统的有机结合；最后,为确保基于多Agent的钢铁生产复杂物流仿真系统模型的有效性和正确性,在对多Agent系统工程建模方法扩展的基础上实现对复杂物流仿真系统的分析设计,建立了相应的复杂物流仿真系统模型,并进一步借助Agent建模工具实现对整个仿真系统模型的分析设计和模型验证。
根据昆钢炼钢厂的生产实际,建立相应的基于多Agent的钢铁生产物流仿真模型,并将仿真结果和实际数据相对比,结果表明：
①基于多Agent的钢铁生产复杂物流系统仿真模型是正确有效的,可以根据实际规模要求灵活搭建基于工序及工位的仿真模型。在相似的输入条件下,仿真结果与实际系统中转炉至连铸区间的物流平均流通时间进行对比分析进一步表明：基于多Agent的钢铁生产复杂物流系统仿真模型和实际系统没有明显差别,能正确反映炼钢厂的复杂物流实况。
②基于多Agent的钢铁生产复杂物流系统仿真模型可以根据不同生产流程特点构建相应的仿真模型,仿真可揭示不同生产流程在不同生产条件下的生产瓶颈,脱硫工序在有混铁炉和取消混铁炉的炼钢生产流程中均为生产瓶颈,对于取消混铁炉的炼钢生产流程,当铁水进厂节奏较慢时(3罐/60min),脱硫工序为生产瓶颈,当铁水进厂节奏提高到一定程度(≥5罐/60min)后,转炉工序成为生产瓶颈。
③利用仿真模型可为不同钢铁生产流程下制定提高生产效率、多台连铸机同时实现连浇的策略提供决策支持。比较有混铁炉和取消混铁炉的炼钢生产流程,当铁水进厂节奏比较慢时(3罐/60min),加快铁水进厂节奏或加快转炉冶炼周期有利于生产效率和连浇百分比的提高,而对于取消混铁炉的生产流程,此时较长的转炉冶炼周期反而有利于生产效率的提高；铁水进厂节奏达到一定程度后(≥5罐/60min),铁水进厂节奏或转炉冶炼周期的加快对提高生产效率和连浇百分比均没有明显效果,对于取消混铁炉的炼钢生产流程,加快转炉冶炼周期有利于提高系统生产效率。
④基于多Agent的钢铁生产复杂物流系统仿真模型具有的通用性、实用性和灵活性,能正确模拟炼钢生产的复杂物流特性,可针对不同生产过程进行系统诊断和预演,根据仿真模型的仿真结果,可实现对钢铁生产组织和生产流程物流的优化管理,为全连铸生产管理及物流控制的改进提供决策支持。
本文研究表明：基于多Agent的钢铁生产复杂物流系统仿真模型建模方法在表达炼钢生产物流特性、揭示复杂物流系统运行机制方面更有效,能更好的满足当前分布式复杂系统的建模需要,具有对各种炼钢生产物流系统进行灵活建模且仿真适应性较强。该建模方法的提出和实现为复杂制造流程的建模与物流仿真研究提供了新的手段和方法。
At present, because of resources diminishing, environment deteriorating, constraint of environmental policy increasing, steel production enterprise must achieve the targets of energy saving and emission reduction, further improve its adaptability and international competition ability, realize resource recycling and itself sustainable development. So, it is very necessary for steel production enterprise to research, analyze and optimize the logistics systems.
Steel production is a complex system with more processes, more workstation, lager spatial span and more product categories. It is a key factor that affects the product categories, quality and yield in logistics system of Steel production. So, it is the key to understand logistics rules, formulate reasonable production plans and scheduling for guaranteeing production logistics unblocked, increasing production and quality, and reducing the cost.
Because of its autonomy, social ability, reactivity, pro-activity, mobility and reasoning, agent can be used to realize the dynamic, uncertain environment, large-scale software systems. Multi-Agent System is defined as a system of a population of autonomous agents, which interact with each other to form the solution, according to environment and knowledge reasoning, learning etc. It can improve overall system performance and meet the system requirements of mobility, flexibility and openness.Agent-oriented methodology has provided a very natural method to understanding, modeling and development the complex system. It made the distributed structure easier, intelligent and robust, so it is recognized as a novel mythology for complex systems.
The aims of this dissertation is to explore new simulation method not only describing the complex characteristics of logistics in steel production process, but also responding to the dynamic characteristics of logistics system and be widely used for different forms of steel production flow, which has a very important significance in both theory and practice.
Aimed to the new request of flexibility, pliability and compatibility and the complex characteristics of logistics in steel production process, because of Agent technical superiority and Multi-Agent system merit, which could be used to overcome the shortcoming of the other's modelling method.so, simulation model of logistics in steel production process based on Multi-Agent was presented. In the model, the logistics system in steel production process has been defined as a Multi-Agent System. Based on the versatile principles, the technology of analyzing, designing and realizing for simulation system model was studied from the standpoint of software engineering. First of all, based on understanding and grasping the characteristics of complexity in steel production process, system function of simulation model of logistics in steel production process based on Multi-Agent has been abstracted and defined by introducing Multi-Agent technology in modelling process.Secondly, interactive models of agents have been established by analyzing the types and structures. Internal and external action of agents has been described by extending the structure and function of Petri Net and developing behavior theory of agent and interactive behavior theory of agents.Next, because of the special requirement of flexibility and mobility for logistics system in steel production process, the transportation system has played an important role in the whole logistics system, so the transportation system was combined with the whole logistics system in steel production process by exploring the operation mechanism of crane. Finally, to ensure the effectiveness and correctness of simulation model based on Multi-Agent, the logistics simulation system has been realized by extending the method of Multi-Agent System Engineering, and the system model has been designed and verified by the Agent Tool.
According to actual production conditions of No.3 steel-making plant in Kunming Iron and Steel Co., LTD,the simulation model of logistics system in steel production process based on Multi-Agent was established and the simulation results was compared with actual data, so the results show:
The simulation model of logistics system in steel production process based on Multi-Agent is correct and effective, according to the actual scale requirement, which was flexibly established by different processes and workstation. In similar input conditions, logistics average circulation time from converter to continuous casting was compared with simulation results, and the results further show:the simulation model of logistics system in steel production process based on Multi-Agent is consistent with actual system, which can correctly reflect production logistics system in steel production process.
The simulation model of logistics system in steel production process based on Multi-Agent can build up model for different steel-making process, and simulation can disclosure bottleneck of different steel-making process under different productions, such as desulfurization process is bottleneck in steel-making process with the mixer furnace process, as for steel-making process without mixer furnace process, desulfurization process is bottleneck when the arrival of the hot metal from blast furnace is three ladles percent sixty minute, but when the arrival of the hot metal from blast furnace is more than five ladles percent sixty minute, converter process is bottleneck.
Simulation analysis using this model is help to make decision for improving production efficiency and continuous eating of different steel-making process. Through comparing the simulation results of steel-making process with mixer furnace process and the one without mixer furnace process, it is shown that when the arrival of the hot metal from blast furnace is three ladles percent sixty minute, it is good for improving production efficiency to accelerate the arrival of the hot metal from blast furnace and the production time of converter process, for steel-making process without mixer furnace process the production time of converter process should be longer in order to improve casting. The arrival of the hot metal from blast furnace is almost independent on production efficiency and continuous casting when it exceeds five ladles percent sixty minute, it is benefit for improve production efficiency to accelerate the production time of converter process for steel-making process without mixer furnace process.
The simulation model of logistics system in steel production process based on Multi-Agent has good generality, practicability and flexibility, which can be used to simulate logistics characteristics of metallurgical production and diagnose and rehearse logistics system by different production process. According to the system simulation results of simulation model, production organization and production process in steel production process was optimized and decision support for the whole continuous casting production management and logistics control was provided.
The simulation results show that simulation model of logistics system in steel production process based on Multi-Agent is convenience and validity to express the characteristics of logistics in steel production process and disclosure the rule of complex logistics, has flexibility and adaptability for modeling and simulating in different steel production process, and can better satisfy the current requirement of distributed complex system modeling. This model method provides a new tool and route for simulation logistics in complex steel manufacturing process.
钢铁生产是一个多工序、多工位、空间跨度大、生产品种多的具有动态和不确定性的复杂物流系统。钢铁生产复杂物流系统是影响产品品种、质量和产量的关键因素,认识其生产物流规律,进行合理的生产计划及调度,是实现生产物流畅通的保障,是提高产品产量和降低生产成本的关键。
Agent的自主性、社会性、反应性、主动性、移动性、理智性等特性可以用来实现动态的、不确定环境的、大规模的软件系统。多Agent系统把多个Agent有效组织起来,相互协作和交流,形成问题的求解环境,并根据环境和交流知识进行推理、学习等,能够有效实现系统整体性能的提高和适应系统的灵活性、柔性、开放性等要求。面向Agent的开发方法已经成为软件工程领域的新趋势,为复杂系统的理解、建模、开发提供了一种很自然的方法,它使分布式的结构变得更简单、智能化和具有鲁棒性。
本文旨在利用多Agent技术探索一种既能描述钢铁生产过程复杂物流系统特性,又能反应物流系统的动态特征,并能对不同形式的钢铁生产流程具有广泛适应性的建模仿真方法和软件工程设计方法,在理论和实践方面均具有非常重要的意义。
针对钢铁生产过程灵活性、柔性和适应性的要求及其物流系统的复杂性特点,根据Agent技术优势及多Agent系统优点,其应用在钢铁生产复杂物流仿真系统中时可有效克服已有建模方法的不足,提出了基于多Agent的钢铁生产复杂物流仿真系统建模方法,把钢铁生产过程复杂的物流系统抽象为一个多Agent系统,基于通用性原则对仿真系统模型进行软件工程的分析、设计与实现技术研究。首先在对钢铁生产物流系统复杂性充分认识和把握的基础上,把多Agent技术引入到钢铁生产复杂物流系统的建模过程中,实现对基于多Agent的钢铁生产复杂物流仿真系统的系统功能分层抽象和定义；其次,通过分析仿真系统中Agent类型及结构,在对Petri网结构和功能进行扩展和Agent行为理论及Agent间交互行为理论拓展的基础上,建立了仿真系统中Agent的行为及其之间的交互模型,并借助形式化描述工具Petri网实现Agent内部动作和外部动作及其之间交互的形式化建模；另外,由于钢铁生产过程中物流系统灵活性和柔性的特殊要求,致使钢铁生产过程中的运输系统在整个物流系统中具有举足轻重的地位,本文就运输系统中天车运行机制进行详细分析和研究的基础上,实现了运输系统和物流仿真系统的有机结合；最后,为确保基于多Agent的钢铁生产复杂物流仿真系统模型的有效性和正确性,在对多Agent系统工程建模方法扩展的基础上实现对复杂物流仿真系统的分析设计,建立了相应的复杂物流仿真系统模型,并进一步借助Agent建模工具实现对整个仿真系统模型的分析设计和模型验证。
根据昆钢炼钢厂的生产实际,建立相应的基于多Agent的钢铁生产物流仿真模型,并将仿真结果和实际数据相对比,结果表明：
①基于多Agent的钢铁生产复杂物流系统仿真模型是正确有效的,可以根据实际规模要求灵活搭建基于工序及工位的仿真模型。在相似的输入条件下,仿真结果与实际系统中转炉至连铸区间的物流平均流通时间进行对比分析进一步表明：基于多Agent的钢铁生产复杂物流系统仿真模型和实际系统没有明显差别,能正确反映炼钢厂的复杂物流实况。
②基于多Agent的钢铁生产复杂物流系统仿真模型可以根据不同生产流程特点构建相应的仿真模型,仿真可揭示不同生产流程在不同生产条件下的生产瓶颈,脱硫工序在有混铁炉和取消混铁炉的炼钢生产流程中均为生产瓶颈,对于取消混铁炉的炼钢生产流程,当铁水进厂节奏较慢时(3罐/60min),脱硫工序为生产瓶颈,当铁水进厂节奏提高到一定程度(≥5罐/60min)后,转炉工序成为生产瓶颈。
③利用仿真模型可为不同钢铁生产流程下制定提高生产效率、多台连铸机同时实现连浇的策略提供决策支持。比较有混铁炉和取消混铁炉的炼钢生产流程,当铁水进厂节奏比较慢时(3罐/60min),加快铁水进厂节奏或加快转炉冶炼周期有利于生产效率和连浇百分比的提高,而对于取消混铁炉的生产流程,此时较长的转炉冶炼周期反而有利于生产效率的提高；铁水进厂节奏达到一定程度后(≥5罐/60min),铁水进厂节奏或转炉冶炼周期的加快对提高生产效率和连浇百分比均没有明显效果,对于取消混铁炉的炼钢生产流程,加快转炉冶炼周期有利于提高系统生产效率。
④基于多Agent的钢铁生产复杂物流系统仿真模型具有的通用性、实用性和灵活性,能正确模拟炼钢生产的复杂物流特性,可针对不同生产过程进行系统诊断和预演,根据仿真模型的仿真结果,可实现对钢铁生产组织和生产流程物流的优化管理,为全连铸生产管理及物流控制的改进提供决策支持。
本文研究表明：基于多Agent的钢铁生产复杂物流系统仿真模型建模方法在表达炼钢生产物流特性、揭示复杂物流系统运行机制方面更有效,能更好的满足当前分布式复杂系统的建模需要,具有对各种炼钢生产物流系统进行灵活建模且仿真适应性较强。该建模方法的提出和实现为复杂制造流程的建模与物流仿真研究提供了新的手段和方法。
At present, because of resources diminishing, environment deteriorating, constraint of environmental policy increasing, steel production enterprise must achieve the targets of energy saving and emission reduction, further improve its adaptability and international competition ability, realize resource recycling and itself sustainable development. So, it is very necessary for steel production enterprise to research, analyze and optimize the logistics systems.
Steel production is a complex system with more processes, more workstation, lager spatial span and more product categories. It is a key factor that affects the product categories, quality and yield in logistics system of Steel production. So, it is the key to understand logistics rules, formulate reasonable production plans and scheduling for guaranteeing production logistics unblocked, increasing production and quality, and reducing the cost.
Because of its autonomy, social ability, reactivity, pro-activity, mobility and reasoning, agent can be used to realize the dynamic, uncertain environment, large-scale software systems. Multi-Agent System is defined as a system of a population of autonomous agents, which interact with each other to form the solution, according to environment and knowledge reasoning, learning etc. It can improve overall system performance and meet the system requirements of mobility, flexibility and openness.Agent-oriented methodology has provided a very natural method to understanding, modeling and development the complex system. It made the distributed structure easier, intelligent and robust, so it is recognized as a novel mythology for complex systems.
The aims of this dissertation is to explore new simulation method not only describing the complex characteristics of logistics in steel production process, but also responding to the dynamic characteristics of logistics system and be widely used for different forms of steel production flow, which has a very important significance in both theory and practice.
Aimed to the new request of flexibility, pliability and compatibility and the complex characteristics of logistics in steel production process, because of Agent technical superiority and Multi-Agent system merit, which could be used to overcome the shortcoming of the other's modelling method.so, simulation model of logistics in steel production process based on Multi-Agent was presented. In the model, the logistics system in steel production process has been defined as a Multi-Agent System. Based on the versatile principles, the technology of analyzing, designing and realizing for simulation system model was studied from the standpoint of software engineering. First of all, based on understanding and grasping the characteristics of complexity in steel production process, system function of simulation model of logistics in steel production process based on Multi-Agent has been abstracted and defined by introducing Multi-Agent technology in modelling process.Secondly, interactive models of agents have been established by analyzing the types and structures. Internal and external action of agents has been described by extending the structure and function of Petri Net and developing behavior theory of agent and interactive behavior theory of agents.Next, because of the special requirement of flexibility and mobility for logistics system in steel production process, the transportation system has played an important role in the whole logistics system, so the transportation system was combined with the whole logistics system in steel production process by exploring the operation mechanism of crane. Finally, to ensure the effectiveness and correctness of simulation model based on Multi-Agent, the logistics simulation system has been realized by extending the method of Multi-Agent System Engineering, and the system model has been designed and verified by the Agent Tool.
According to actual production conditions of No.3 steel-making plant in Kunming Iron and Steel Co., LTD,the simulation model of logistics system in steel production process based on Multi-Agent was established and the simulation results was compared with actual data, so the results show:
The simulation model of logistics system in steel production process based on Multi-Agent is correct and effective, according to the actual scale requirement, which was flexibly established by different processes and workstation. In similar input conditions, logistics average circulation time from converter to continuous casting was compared with simulation results, and the results further show:the simulation model of logistics system in steel production process based on Multi-Agent is consistent with actual system, which can correctly reflect production logistics system in steel production process.
The simulation model of logistics system in steel production process based on Multi-Agent can build up model for different steel-making process, and simulation can disclosure bottleneck of different steel-making process under different productions, such as desulfurization process is bottleneck in steel-making process with the mixer furnace process, as for steel-making process without mixer furnace process, desulfurization process is bottleneck when the arrival of the hot metal from blast furnace is three ladles percent sixty minute, but when the arrival of the hot metal from blast furnace is more than five ladles percent sixty minute, converter process is bottleneck.
Simulation analysis using this model is help to make decision for improving production efficiency and continuous eating of different steel-making process. Through comparing the simulation results of steel-making process with mixer furnace process and the one without mixer furnace process, it is shown that when the arrival of the hot metal from blast furnace is three ladles percent sixty minute, it is good for improving production efficiency to accelerate the arrival of the hot metal from blast furnace and the production time of converter process, for steel-making process without mixer furnace process the production time of converter process should be longer in order to improve casting. The arrival of the hot metal from blast furnace is almost independent on production efficiency and continuous casting when it exceeds five ladles percent sixty minute, it is benefit for improve production efficiency to accelerate the production time of converter process for steel-making process without mixer furnace process.
The simulation model of logistics system in steel production process based on Multi-Agent has good generality, practicability and flexibility, which can be used to simulate logistics characteristics of metallurgical production and diagnose and rehearse logistics system by different production process. According to the system simulation results of simulation model, production organization and production process in steel production process was optimized and decision support for the whole continuous casting production management and logistics control was provided.
The simulation results show that simulation model of logistics system in steel production process based on Multi-Agent is convenience and validity to express the characteristics of logistics in steel production process and disclosure the rule of complex logistics, has flexibility and adaptability for modeling and simulating in different steel production process, and can better satisfy the current requirement of distributed complex system modeling. This model method provides a new tool and route for simulation logistics in complex steel manufacturing process.
引文
[1]唐立新.CIMS下生产批量计划理论及其应用[M].北京：科学出版社.1999.
[2]殷瑞钰.冶金流程工程学[M].北京：冶金工业出版社,2004.
[3]百度百科.http://baike.baidu.com/view/909679. htm.2011-4-14.
[4]吴清一,王转.物流系统工程[M].北京：高等教育出版社,2004.
[5]殷瑞钰.钢铁制造过程的多维物流控制系统[J].金属学报1997,33(1)：29-38.
[6]杨辉,张长森,曹枫等.钢铁企业生产物流合理化研究[J].商业时代,2009(23)：104-105.
[7]陈森发.复杂系统建模理论与方法[M].南京：东南大学出版社,2005.
[8]宋学锋.复杂性、复杂系统与复杂性科学[J].中国科学基金,2003,(5)：262-269.
[9]郝柏林.分岔、混沌、奇怪吸引子、湍流及其它[J].物理学进展,1983,3(3)：340-398
[10]钱学森,于景元.一个科学领域-开放的复杂巨系统及其方法[J].自然杂志,1990,13(1)：3-10.
[11]方美琪,张树人.复杂系统建模与仿真[M].北京：中国人民大学出版社,2002.
[12]迟延,谭跃进,邓宏钟.基于CGP模型的涌现研究[J].系统工程与电子技术,2002,24(1)：10-13.
[13]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3)：416-420.
[14]廖守亿,戴金海.基于Agent的分布仿真中的通信系统设计与实现[J].系统仿真学报,2006,14(2)：1006-1009,1014.
[15]邹吴飞,夏国平,杨方廷.基于自组织算法的改进型GAANN预测模型[J].中国管理科学,2005,13(6)：75-80.
[16]刘晓平,唐益明,郑利平.复杂系统与复杂系统仿真研究综述[J].系统仿真学报,2008,20(23)：6303-6315.
[17]吴哲辉.Petri网导论[M].北京：机械工业出版社,2006.
[18]K Jensen. Colored Petri Nets:Basic Concept, Analysis Methods And Practical Use[M]. Berlin:Spring-Verlag,1997.
[19]唐达,刘丹妮.基于层次时间Petri网的工作流模型可达性分析方法[J].计算机集成制造系统,2006,12(4)：487-493.
[20]刘雪艳,吴慧欣,张强等.基于面向对象时间Petri网的密码协议分析[J].计算机工程,2009,35(13)：156-159.
[21]瞿继双,戴金海.复杂离散实时系统任务／资源图建模方法研究[J].系统仿真学报,2000,12(6)：600-603.
[22]董明,查建中,杜玉明等.用面向对象的框架语言实现离散事件仿真[J].天津大学学报,1997,30(1)：43-48.
[23]B. P Zeigler. Multifaceted modeling and discrete event simulation [M].London and Orando,FL, USA:Academic Press,1984.
[24]P. G Hawkins, D. J Woollons. Failure modes and effects analysis of complex engineering systems using functional models[J]. Artificial Intelligence in Engineering(S0954-1810),1998,12(4):375-397.
[25]葛建新,杨莉.基于整数规划的几何因果定性推理建模和求解[J].计算机学报,1997,20(12)：1095-1104.
[26]顾宇红,石纯一.基于分层因果关系的定性推理方法[J].软件学报,1998,9(12)：884-888.
[27]Y. Q Zhao, D. F Zhu, H Wang et al.Modeling and Simulating for the Manufacturing Process of Metallurgy based on System Dynamics.2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics,2010, Wuhan,105-109.
[28]赵业清,王华.区域钢铁产品生命周期物质流分析—基于系统动力学方法的建模与仿真.工业加热,2009,38(1)：9-13.
[29]朱大智,吴俊,谭跃进等.度秩函数：一个新的复杂网络统计特征[J].复杂系统与复杂性科学,2006,3(4)：28-34.
[30]张嗣瀛.复杂系统、复杂网络自相似结构的涌现规律[J].复杂系统与复杂性科学,2006,3(4)：41-51.
[31]S Boccaletti,V Latora, Y Moreno, et al. Complex Networks:Structure and Dynamics[J]. Complex Systems and Complexity Science,2006,3(3):56-94.
[32]S Boccaletti, V Latora, Y Moreno, et al. Complex Networks:Structure and Dynamics[J]. Complex Systems and Complexity Science,2006,3(4):52-90.
[33]毛嫒,刘杰,李伯虎.基于元模型的复杂系统建模方法研究[J].系统仿真学报,2002,14(4)：411-414,454.
[34]康卓,黄竞伟,李艳等.复杂系统数据挖掘的多尺度混合算法[J].软件学报,2003,14(7)：1229-1237.
[35]胡晓峰.战争复杂性与信息化战争模拟[J].系统仿真学报,2006,18(12)：3572-3580.
[36]崔霞,戴汝为,李耀东.群体智慧在综合集成研讨厅体系中的涌现.系统仿真学报,2003,15(1)：146-153.
[37]E Kondili,C Pantelidis, R Sargent. A General Algorithm for Short-term Scheduling of Batch Operations-I. MILP Formulation[J]. Computer & Chemical Engineering,1993,17(22):211-227.
[38]V.T Voudouris, I.E Grossmann. MILP Model for Scheduling and Design of a Special Class of Multipurpose Batch Plants [J]. Computer & Chemical Engineering,1996,20(11):1335-1360.
[39]C. A Mendez, J Cerda. An Efficient MILP Continuous-time Formulation for Short-term Scheduling of Multiproduct Continuous Facilities [J]. Computer & Chemical Engineering,2002,26(4-5):687-695.
[40]C. A Mendez, J Cerda. Dynamic Scheduling in Multi-product Batch Plants[J]. Computer & Chemical Engineering,2003,27(8-9):1247-1259.
[41]G Schilling, C. C Pantelides. A Simple Continuous-time Process Scheduling Formulation and A Novel Solution Algorithm[J]. Computer & Chemical Engineering,1996,20(supp2):1221-1226.
[42]M Yuceer, R Berber. A Heuristic Approach for Solution of MINLP Problem for Production Scheduling in Preemptive Mode[J]. Chemical Engineering &Processing,2005,44(9):933-940.
[43]C. N Potts, V. L Wassenhove.A Branch and Bound Algorithm for the Total Weighted Tardiness Problem [J]. Operation Reseach,1985,33 (3):367-377.
[44]刘晓霞.基于遗传分枝定界算法的Flow-shop调度问题[J].控制工程,2003,10(3)：205-208,232.
[45]M. W Park, Y. D Kim. A Branch and Bound Algorithm for a Production Scheduling Problem in an Assembly System under Due Date Constraints [J]. European Journal of Operational Research,2000,123(3):504-518.
[46]G Rabadia, M Mollaghasemib, G.C Anagnostopoulos. A Branch-and-Bound Algorithm for the Early/Tardy Machine Scheduling Problem with a Common Due-date and Sequence-dependent Setup Time[J]. Computers & Operations Research,2004,31(10):1727-1751.
[47]熊锐,张俊杰,卢改林.用拉氏松弛法解决FMS中一类特殊的生产调度问题[J].西安工业学院学报,1997,17(4)：272-277.
[48]熊锐,陈浩勋,胡保生.一种生产计划与车间调度的集成模型及其拉氏松弛求解法[J].西安电子科技大学学报,1996,23(4)：509-516.
[49]L. X Tang, B.P Luh, J. Y Liu et al. Steel-making Process Scheduling Using Lagrangian Relaxation[J]. International Journal of Production Research,2002,40(1):55-70.
[50]张凤梅,洪运国.基于多机调度问题的动态规划算法[J].计算机技术与发展,2006,16(3)：61-62,65.
[51]H Park, Y Hong, S. Y Chang. An Efficient Scheduling Algorithm for the Hot Coil Making in the Steel Mini-mill[J]. Production Planning & Contol, 2002,13, (3):298-306.
[52]黄可为,卢克斌,汪定伟.炼钢组炉问题优化模型及其动态规划算法[J].东北大学学报(自然科学版),2006,27(2)：138-141.
[53]吴锦标,刘再明,尹小玲等.基于排队论的一个物流模型[J].系统工程理论与实践,2009,29(9)：78-83.
[54]吴耀华,王胜力,王常香.排队论在物流规划中的应用[J].物流科技,2004,27(105)：53-55.
[55]Y. W Kim, T Suzuki, T Narikiyo. FMS scheduling based on timed Petri Net model and reactive graph research [J]. Applied Mathematical Modelling, 2007,31(6):955-970.
[56]W. M Zuberek, W Kubiak. Timed Petri Nets in Modeling and Analysis of Simple Schedules for Manufacturing Cells[J]. Computers and Mathematics with Applications,1999,37(11-12):191-206.
[57]李霄峰,徐立云,邵惠鹤等.炼钢连铸系统的动态调度模型和启发式调度算法[J].上海交通大学学报,2001,35(11)：1658-1662.
[58]嵇振平,陈文明,于戈.分层有色Petri Net(HCPN)及其在宝钢炼钢连铸生产物流系统仿真建模中的应用[J].冶金自动化,2002,(2)：6-9.
[59]昊亚丽,曾建潮,卫军胡,等.一种基于混合Petri网的批过程动态调度算法[J].西安交通大学学报,2002,36(2)：147-151.
[60]何腊梅,郑忠,高小强.基于时间倒推和流程仿真炼钢生产计划的制定[J].重庆大学学报(自然科学版),2006,29(9)：60-63.
[61]何腊梅.炼钢生产物流的细胞自动机仿真模型研究[D].重庆：重庆大学硕士论文,2003.
[62]郑忠,何腊梅,高小强.基于细胞自动机的流程仿真方法在炼钢生产中的应用[A].2005年中国钢铁年会论文集[C],冶金工业出版社,北京,2005,718-723.
[63]李陶深.人工智能[M].重庆：重庆大学出版社,2002.
[64]D. F Guo, T. K Li. Rescheduling Algorithm for Steelmaking-Continuous Casting[A].2007 Second IEEE Conference on Industrial Electronics and Applications[C],2007,1421-1425.
[65]刘光航,李铁克.炼钢-连铸生产调度模型及启发式算法[J].系统工程,2002,20(6)：44-48.
[66]郝文育,李亚白,王宁生.一种启发式车间作业调度算法的研究与应用[J].机械科学与技术,2005,24(7)：861-864.
[67]张德富,李新.求解作业车间调度问题的快速启发式算法[J].计算机集成制造系统,2005,11(2)：237-241.
[68]S.S Panwalkar. A survey of Scheduling Rules[J], Operations Research, 1977,25(1):45-61.
[69]林海凡,李东波,唐敦兵等.生产调度问题的启发式遗产算法[J].机械科学与技术,2000,19(5)：790-792.
[70]唐立新,杨自厚,王梦光等.CIMS中带多资源的CLSP问题的遗传启发式算法[J],系统工程理论与实践,1997,(4)：39-44.
[71]冯振军,杨根科,杜斌等.炼钢连铸调度的启发式和线性规划两步优化算法[J],冶金自动化,2005,(4)：18-22.
[72]K. S Metaxiotis, D Askounis, J Psarras. Expert Systems in Production Planning and Scheduling:A State-of-the-art Survey[J].Journal of Intelligent Manufacturing,2002,13(4):253-260.
[73]樊俊飞,李永如,姚海石.宝钢转炉吹炼控制模拟在线专家系统[J].宝钢技术,1996,(4)：50-54,58.
[74]龚宇,熊光楞.机器学习在智能车间调度系统中的应用[J].控制与决策,1997,12(3)：222-227,233.
[75]李蓓智,杨建国,丁惠敏.基于生物免疫机理的智能调度系统建模与仿真[J].计算机集成制造系统,2002,8(6)：446450.
[76]刘研,张俐丽,刘涛.遗传算法在车间作业调度中设计与应用[J].自动化技术与应用,2009,28(12)：8-12.
[77]李铁克,王伟玲,张文学.基于文化遗传算法求解柔性作业车间调度问题[J].计算机集成制造系统,2010,16(4)：861-866.
[78]梁迪,谢里阳,隋天中等.基于遗传和禁忌搜索算法求解车间调度优化问题[J].计算机应用,2006,26(4)：857-860.
[79]邓泽林,黄文奇,周立刚.求解车间作业调度问题的快速禁忌搜索算法[J].华中科技大学学报(自然科学版),2003,31(11)：1-3.
[80]黄明,闰淑娟,梁旭.遗传算法和禁忌搜索算法在车问调度中的研究进展.工业控制计算机,2004,17(2)：4-5.
[81]杨玉文,王俊茹,张宏芳.求解Flow shop调度问题的混合禁忌搜索算法[J].哈尔滨师范大学自然科学学报,2007,23(3)：59-61.
[82]潘全科,段俊华,赵清理,朱剑英.解决车间调度问题的改进模拟退火算法[J].机械科学与技术,2007,26(1)：112-114.
[83]吴大为,陆涛栋,刘晓冰等.求解作业车间调度问题的并行模拟退火算法[J].计算机集成制造系统,2005,11(6)：847-850
[84]薛拾贝,席裕庚.用蚁群算法求解Job-Shop问题的机器分解方法[J].计算机仿真,2008,25(11)：187-190,225.
[85]张维存,郑丕谔,吴晓丹.蚁群遗传算法求解能力约束的柔性作业车间调度问题[J].计算机集成制造系统,2007,13(2)：333-337,362.
[86]Y. Q Yun, J. G Sun, J. Y Zhang et al. A hybrid discrete particle swarm algorithm for open shop problems[C]. Proceedings of the 6th International Conference on Simulated Evolution and Learning(SEAL 2006). Hefei, China,2006:158—165.
[87]张长胜,孙杰贵,欧阳丹彤等.求解车间调度问题的自适应混合粒子群算法[J].计算机学报,2009,32(11)：2137-2146.
[88]贾兆红,陈华平,孙耀晖.混合粒子群算法在柔性工作车间调度中的应用[J].系统仿真学报.2007,19(20)：4743-4747.
[89]S Yang, D. A Wang. A New Adaptive Neural Network and Heuristics Hybrid Approach for Job-Shop Schedul ing [J]. Computers & Operations Research, 2001,28(10):955-971.
[90]王万良,吴启迪,徐新黎.基于Hopfield神经网络的作业车间生产调度方法[J].自动化学报,2002,28(5)：838-844.
[91]徐新黎,王万良,吴启迪.改进计算能量函数下作业车间调度的混沌神经网络方法[J].控制理论与应用,2004,21(2)：311-315.
[92]马鑫,梁艳春.基于GPGP协同机制的多Agent车间调度方法研究[J].计算机研究与发展,2008,45(3)：479-486.
[93]牟文恒,吕志民,唐荻.多代理机制在炼钢连铸排程中的应用研究[J].钢铁,2006,41(5)：2932.
[94]宋娟.基于CNP的多Agent车问调度系统设计与实现[J].工业控制计算机,2010,23(6)：82-84.
[95]李应,杨善林,左春荣.敏捷制造系统的多Agent结构与调度方法研究[J].合肥工业大学学报(自然科学版),2005,28(5)：541-545.
[96]曾齐红.基于遗传算法求解炼钢—连铸浇次排序问题[J].鞍山师范学院学报,2004,6(6)：70—73.
[97]崔志华,高慧敏,曾建潮.一种求解连铸生产调度问题的非线性遗传算法[J].计算机工程与应用,2004,(35)：205-207.
[98]M Liu, C Wu. Identical Parallel Machine Scheduling Problem for Minimizing the Makespan Using Genetic Algorithm Combined by Simulated Annealing [J].Chinese Journal of Electronics.1998,7(10):317-3321.
[99]陈璐,奚立峰,蔡建国,等.一种求解带有阻塞限制的混合流水车间的禁忌搜索算法[J].上海交通大学学报,2006,40(5)：854-857.
[100]朱道飞.钢厂生产作业计划与调度的自组织优化方法研究[D].重庆：重庆大学博士论文,2008.
[101]郑忠,朱道飞,高小强.基于蚁群算法的炼钢-连铸作业计划编制方法.北京科技大学学报,2009,31(4)：504-510.
[102]朱道飞,郑忠,高小强.炼钢-连铸作业计划的遗传优化模型.钢铁,2008,43(7)：26-31.
[103]ZHU Dao-fei,WANG Hua, WANG Shi-bo et al. Hybrid GeneticAlgorithm-based Production Planning for Steel-making and Continuous Casting Process[C]. ICEEAC2010, V2,484-488.
[104]朱道飞,王华,马长波.准时制下炼钢生产计划时间并行顺推编制方法.计算机工程与应用,2010,46(32)：236-240.
[105]郑忠,朱道飞,高小强.混合流程作业计划编制的时间并行倒推算法.计算机集成制造系统,2008,14(4)：749-756
[106]郑忠,朱道飞,高小强.钢厂炼钢-连铸生产调度及重计划方法.重庆大学学报(自然科学版),2008,31(7)：820-824.
[107]ZHU Dao-fei, ZHENG Zhong, GAO Xiao-qiang. Intelligent Optimization Based Production Planning and Simulation Analysis for Steel making and Continuous Casting Process.Journal of IRON AND STEEL RESEARCH, INTERNATIONAL.2010,17 (9):19-24,30.
[108]Visual8 Simulation Modeling & Decision Support Solutions. http://www. visual8. com/v8s. html.2011-4-14.
[109JSIMATIC IT. http://www.ca800.com/product/html/2007-6-28/p101982. html.2011-4-14.
[110]PSI Metals. http://www.steelplanner.com.2011-4-14.
[111]IBM ILOG Optimization. http://www-01.ibm.com/software/websphere/ products/optimization/.2011-4-14.
[112]Gensym|Business Rule Management. http://www.gensym.com.2011-4-14.
[113]S. C Hamoen, D. J Moens.Logistic Simulator for Steel Producting Factories[C]. Proceedings of the 2002 Winter Simulation Conference. 2002,1315-1318.
[114]常春光,胡琨元,汪定伟等.钢铁生产动态调度理论研究与工程应用综述[J].信息与控制,2003,32(6)：531-537.
[115]M Numao, S Morishita. Scheplan-A Scheduling Expert for Steel-Making Process[A]. Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications[C], Hitachi,1988,467-472.
[116]K. R Ryu,0. K Lim, H. K Lee. The Advanced Production and Product Scheduling Control System at the Kwangyang Works[C]. Proceedings of International Conference on Computerized Production Control in Steel Plant.South Korea:Posdata Corp,1993,299-308.
[117]K Stohl, W Snopek. VAI-Schedex:A Hybrid Expert System for Cooperative Production Scheduling in a Steel Plant[C]. Proceedings of International Conference on Computerized Production Control in Steel Plant.South Korea:Korea Press.1993,207-217.
[118]杨越.钢铁厂综合化生产调度系统[J].冶金自动化,1998,(5)：22-25,35.
[119]A Stephens. Integrated Scheduling of Steel Production[J]. Asia Steel, 1998:150-153.
[120]徐安军,田乃媛.薄板坯连铸-连轧流程的物流参数分析[J].钢铁研究学报,1998,10(4)：13-16.
[121]邱剑,田乃媛,徐安军等.宝钢铁钢界面运输调度应用程序的开发[J].钢铁,2003,38(5)：73-76.
[122]刘青,田乃媛,殷瑞钰.炼钢厂的运行控制[J].钢铁,2003,38(9)：14-18.
[123]汪开忠,田乃媛.马钢一钢厂三炉对四机生产调度模型研究[J].中国冶金,2005,(6)：29-33.
[124]王佳,田乃媛,徐安军等.新一代大型钢厂生产流程的计算机仿真系统[J].冶金自动化,2007,31(3)：31-35.
[125]徐安军,田乃媛,吴晓东等.宝钢炼钢厂生产调度模拟系统的初步讨论[J].钢铁,1999,3(3)：1-7.
[126]徐幸天,欧阳昭,田乃媛等.全连铸生产计算机调度管理系统[J].钢铁,1998,33(4)：16-19.
[127]高振,唐立新.钢铁企业物流研究概述[J].控制与决策,2001,16(1)：12-15.
[128]李英锦,唐立新,王梦光.基于可视化虚拟现实技术的炼钢-连铸生产调度系统[J].冶金自动化,1999,(4)：14-17,49.
[129]李霄峰,徐立云,邵惠鹤等.柔性炼钢连铸仿真调度系统及其关键技术[J].系统仿真学报,2002,14(2)：207-210,252.
[130]孙羽中,邵惠鹤.物流仿真器在宝钢生产应用中的研究[J].自动化仪表,1999,20(9)：6-8.
[131]常俊林,邵惠鹤.两机零等待流水车间调度问题的启发式算法[J].计算机集成制造系统,2005,11(8)：1147-1153,1162.
[132]李霄峰,徐立云,邵惠鹤等.反向仿真以及在JIT生产调度中的应用[J].系统仿真学报,2001,13(S1)：146-148.
[133]孙福权,唐立新,郑秉霖.炼钢-连铸生产调度专家系统[J].冶金自动化,1998,(6)：31-33.
[134]郑忠,何腊梅,高小强.炼钢生产物流系统仿真的细胞自动机模型[J].钢铁,2004,39(11)：75-78.
[135]刘晓冰,王顺宝,蒙秋男等.钢铁企业集成化生产调度模型的研究[J].微处理机,2007,28(2)：83-86.
[136]薄洪光,刘晓冰,吕艳霞.钢铁企业xBOM过程集成研究[J].工业工程与管理,2009,14(2)：76-80.
[137]薄洪光.钢铁企业集成生产物流管理方法及应用研究[D].大连：大连理工大学博士论文,2008.
[138]马跃,徐然,刘晓冰.钢铁企业先进生产计划和实时生产调度技术的研究[J].制造业自动化,2003,25(3)：35-39.
[139]李宏,李苏剑.武钢第二热轧厂生产物流管理系统[J].物流技术与应 用,2004,(11)：86-88.
[140]李蒙蒙,李苏剑,王冰洁.武钢二热轧生产物流管理信息系统软件设计与描述[J].物流科技,2004,27(112)：75-77.
[141]王冰洁,李苏剑.武钢二热轧板坯库实时生产物流管理信息系统研究与设计[J].物流科技,2004,(12)：61-63.
[142]何志林,李苏剑.武钢2250mm热轧厂生产物流管理系统的研究与开发[J].物流技术,2004,(2)：69-71.
[143]崔建江,罗首章,刘峰等.铁水运输仿真系统的设计与实现[J].系统仿真学报,2003,15(12)：1799-1802.
[144]沈益军.基于SCOR模型的宝钢出厂物流体系分析与改进[D].上海：上海交通大学硕士论文,2008.
[145]沈益军.宝钢出厂物流的SCOR模型分析[J].物流技术,2007,30(6)：65-68.
[146]周忠志,蒋琼珠.基于GPSS-F仿真语言的港口物流仿真自动建模系统[J].交通与计算机,1994,(5)：11-15.
[147]冯新宇,黄凤英.基于GPSS/H和GKS的FMS物流系统的动画仿真[J].计算机工程,1993,19(6)：38-42.
[148]潘燕春,周泓,冯允成.基于Arena的车间作业排序问题建模方法及其仿真优化系统设计[J].计算机集成制造系统,2006,12(3)：389-394.
[149]E Oliveira, K Fischer,0 Stepankova. Multi-agent Systems:Which Research for Which Applications[J]. Robotics and Autonomous Systems, 1998,7(1-2):91-106.
[150]J. Ferber. Multi-agent Systems:An Introduction to Distributed Artificial Intelligence[M]. Addision Wesley, London,1999.
[151]H. S Nwana, L. C Lee, N. R Jennings. Coordination in Software Agent Systems[J]. The British Telecon Technical Journal,1996,14(4):79-88.
[152]D Ouelhadj. Etat de 1'art sur les systemes multi-agents[R]. In:Writing Master report,1996.
[153]G Weiss. Multi-agent Systems:A Modern Approach to Distributed Artificial Intelligence.The MIT Press, Cambridge, Massachusetts, London,England,1999.
[154]张维明,姚莉.智能协作信息技术[M].北京：电子工业出版社,2002.
[155]M Wooldridge. An Introduction to Multi-Agent Systems[M]. Chichester: John Wiley & Sons,2002.
[156]F Zambonelli, A Omicini. Challenges and research directions in agent-oriented software engineering[J]. Autonomous Agents and Multi-Agent Systems,2004,9(3):253-283
[157]J Ferber, M Ghallab. Problematique des univers multi-agents. In:Actes des Journees Nationals du PRC-IA,Cepadus-edition, Toulouse,France, 1988.
[158]P Maes.Artificial Life Meets Entertainment:Life like Autonomous Agents.Communicaion of the ACM,1995,38(11):108-114.
[159]M Wooldridge,N.R Jennings. Intelligent Agents:Theory and Practice. Knowledge Engineering Review,1995,10(2):115-152.
[160]L.P Kaelbing.A Situated Automata Approach to the Design of Embedded Agents[J].Sigart Bulletin,1991,2(4):85—88.
[161]M.E Bratman. Intention, Plans, and Practical Reason [M].Harvad University Press, Cabridge, MA,1987.
[162]A.S Rao, M. P Georgeff. Formal models and decision procedures for Multi-Agent systems[M]. Technical note 61, Australian AI Institute, level 6,171 La Trobe Stree, Melbourne, Australia,1995.
[163]R. A Brooks. Elephants don't play chess[M].In Designing Autonomous Agents,3-15. MIT Press, Cambridge, MA,1990.
[164]M.P Georgeff,A. L Lansky. A System for Reasonning in Dynamic Domains:Fault Diagnosis on the Space Shuttle.Technical Note 375, Artificial Intelligience Center, SRI International, Menlo Park,California,1986.
[165]范辉,李晋江,张晖Agent技术的研究现状与发展趋势[J].微型机与应用,2001,(10)：4-7.
[166]E Durfee,V R Lesser, D.D Corkill. Trends in cooperative distributed problem solving[C]. IEEE Transactions on Knowledge and Data Engineering,1989,1(1):63-83.
[167]J Ferber. Les systemes multi-agents:Apercu general[J]. Techniques et Sciences Informatiques,1997,16(8):979-1012.
[168]E Oliveira, K Fischer,0 Stepankova. Multi-agent systems:which research for which applications [J]. Robotics and Autonomous Systems, 1998,7(1-2):91-106.
[169]N.R Jennings,K Sycara, M Wooldridge. A roadmap of agent research and development[J]. Autonomous Agents and Multi-Agent Systems,1998, 1(1):7-38.
[170]石纯一,张伟,徐晋晖等译.多Agent系统引论[M].北京：电子工业出版社,2003.
[171]M. R Genesereth, S. P Ketchet.Software Agent[J].Communication of the ACM,1993,37(7):48-53.
[172]赵龙文,侯义斌.多Agent系统及其组织结构[J].计算机应用研究,2000(7)：12-15.
[173]王德军,郝永芳.结构化程序设计方法与面向对象的程序设计方法的比较[J].铁路计算机应用,2003,12(11)：13-15.
[174]刁成嘉.面向对象技术导论-系统分析与设计[M].北京：机械工业出版社,2007.
[175]符于江.程序设计中结构化方法和面向对象方法的比较[J].电脑知识与技术,2008(21)：451—452.
[176]姚郑,高文.面向Agent的程序设计[J].软件学报,1997,8(11)：824-831.
[177]P Ciancarini, M Wooldridge. Agent-oriented software engineering [C]. In:Proc. of Intl. Conf. on Software Engineering(ICSE-2000). Los Alamitos.2000,816-817
[178]张纪海.国民经济动员系统建模与仿真研究[M].北京：北京理工大学出版 社,2008.
[179]江志斌Petri网及其在制造系统建模与控制中的应用[M].北京：机械工业出版社,2004.
[180]赵业清,朱道飞,王华等.基于分层时延CPN的轧钢系统建模[J].计算机工程,2011,37(1)：229-231.
[181]Y. Q Zhao, D. F Zhu, H Wang, et al. A Multi-Agent Systems Model for Rolling System Based on Petri Nets.2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics.2010, Wuhan,101-104.
[182]赵业清,朱道飞,王华等.基于Petri网和Agent复杂适应系统建模[J].计算机工程,2011第16期.
[183]宗大华,宗涛.操作系统[M].北京：人民邮电出版社,2006.
[184]C Ramchandani. Analysis of Asynchronous Concurrent Systems by Timed Petri Nets. Project MAC, TR120, M. I. T.,1974.
[185]K Garg. An Approach to Performance Specification of Communication Protocols Using Timed Petri Nets[A]. IEEE Transaction on Software Engineering,1985,11(10):1216-1225.
[186]M. K Melloy. Performance Analysis Using Stochastic Petri Nets [A]. IEEE Transactions on Computers,1982,31 (9):913-917.
[187]P. J Haas, G. S Shedler. Stochastics Petri Net Reparesentation of Discrete Envent Simulations[A]. IEEE, Transaction on software Engineering,1989,15(4):381-393.
[188]K Jenson. Coloured Petri Nets and the invariant method[J]. Theoretical Computer Science,1981, (14):317-336.
[189]J. L Peterson. Petri Net Theory and the Modeling of Systems[M]. Prentice-Hall,1981.
[190]A Camurri, P Franchi, M Vitale. Object-Oriented Approach to High-Level Petri Nets[J]. Microprocessing and Microprogramming,1992, (35):213-220.
[191]L.C Wang. Object-oriented Petri nets for modelling and analysis of automated manufacturing systems[J]. Computer Integrated Manufacturing Systems,1996,9(2):111-125.
[192]Z Jiang, M. J Zuo, P. Y Tu, et al. Object-Oriented Petri Nets with Changeable Structure (OPNs-CS) for Production System Modelling[J]. Int J Adv Manuf Technol,1999, (15):445-458.
[193]薛霄,贾宗璞.面向Agent的软件设计与开发方法[M].北京：电子工业出版社,2009.
[194]AUML Web Site. http://auml.org/.2011-4-14.
[195]M Kunstic, M Bagic. Verification and Validation of Multiagent System Specified by Agent UML[C]. Proceeding of 1st International Conference on Information & Communication Technologies from Theory to Applications 2004,842-853.
[196]SwarmWiki. http://www. swarm.org.2011-4-14.
[197]Repast Suite. http://repast.sourceforge. net.2011-4-14.
[198]Brookings. http://www. brook.edu/es/dynamics/models/ascape/default. htm.2011-4-14.
[199]Netlogo Home Page. http://ccl.northwestern.cdu/netlogo/.2011-4-14.
[200]Starlogo on the Web. http://education.mit.cdu/starlogo/.2011-4-14.
[201]XJ Technologies, http://www.AnyLogic.cn/.2011-4-14.
[202]IRIT-UMR. http://www.irit.fr/cosi/training/evaluationoftools/Evalu ation-of-simulation-tools.htm.2011-4-14.
[203]Software for Agent-based Computational Economics and CAS. http://www.econ.iastate.cdu/tesfatsi/acecode. htm.2011-4-14.
[204]M Veloso, P Stone, K Han. The CMUnited-97 robotic soccer team: Perception and multi-agent control[J]. Robotics and Autonomous Systems,1999, (29):133-143.
[205]D. M Lane, A. C Mcfadzean.Distributed problem solving and realtime mechanisms in robot architecture[J].Engineering Application Intelligence,1994,7(2):105-117.
[206]Y. E Soo,T.S Chang, D Shin et al. Cooperative problem solving in diagnostic agents for chemical processes[J]. Computers and Chemical Engineering,2000, (24):729-734.
[207]T Oyabu, T Misawa, H Kimura,et al. Proposition of sensor agent for estimation of air-pollution direction and its experimental simulation[J]. Materials Science and Engineering,2000,12:89-95.
[208]吴泉源,王怀民.银河分布计算环境[J].软件学报,1997,8(10)：745-751.
[209]T. P Liang, J. S Huang. A framework for applying intelligent agents to support electronic trading[J]. Decision Support Systems,2000, 28:305-317.
[210]K. J Lee, Y.S Chang, J. K Lee. Time-bound negotiation framework for electronic commerce agents[J]. Decision Support Systems,2000, 28:319-331.
[211]J. J Jung, G. S Jo. Brokerage between buyer and seller agents using Constrain Satisfaction Problem models[J]. Decision Support Systems, 2000, (29):293-304.
[212]A Sluga, P Butala, G Bervar. A multi-agent approach to process planning and fabrication in distributed manufacturing[J]. Computers & Industrial Engineering,1998, (35):455-458.
[213]M Schroeder. Towards a visualization of arguing agents[J]. Future Generation Computer Systems,2000, (17):15-26.
[214]K. M Nelson, A Kogan. Virtual auditing agents:the EDGAR Agent challenge [J]. Decision Support Systems,2000, (28):241-253.
[215]N. R Jennings, L.Z Varge, R. P Aarnts. Transforming standalone expert systems into a community of cooperating agents[J]. Engineering Application Artificial Intelligence,1993,6(4):317-331.
[216]P Maes. Agents that reduce work and information overload[J]. Communications of the ACM,1994,37(7):31-40.
[217]张延华,李鹏辉,刘彦春等.基于多智能体钢板质量评估与改进专家系统模型[J].计算机工程与设计,2008,29(6)：1579-1584.
[218]J. P Wangermann, R.F Stengel. Principled negotiation between intelligent agents:a model for air traffic management [J]. Artificial Intelligence in Engineering,1998, (12):177-187.
[219]G Fakas, B Karakostas. A workflow management system based on intelligent collaborative objects[J]. Information and Software Technology,1999, (41):907-915.
[220]G. Q Huang, J Huang,K. L Mak. Agent-based workflow management in collaborative product development on the Internet[J]. Computer Aided Design.2000, (32):133-144.
[221]Y. H Yan, T Kuphal, J Bode. Application of multiagent systems in project management[J]. Int. J. Production Economics,2000,68:185-197.
[222]金愿华,饶运清.基于Multi-Agent的车间作业调度系统的研究[J].机械与电子,2003,(3)：58-61.
[223]李彪.多智能体冶铸轧调度系统的开发研究[D].呼和浩特：内蒙古科技大学硕士论文,2008.
[224]董洁,尹怡欣,彭开香.流程工业多智能体系统协调控制[J].系统工程理论与实践,2008,(10)：119-124.
[225]秦斌,王欣,吴敏等.MA8在焦炉中的应用[J].钢铁,2005,40(10)：25-29.
[226]秦斌,王欣,吴敏等.基于MAS的分布式棒线材轧制在线生产调度系统[J].钢铁,2005,40(9)：49-53.
[227]陈勇,吴国献,林飞龙.多品种多工艺车间作业调度的Multi-Agent建模[J].浙江大学学报(工学版),2009,43(9)：1672-1678.
[228]陈勇,林飞龙.制造企业物流多智能体建模及其应用[J].浙江工业大学学报,2006,34(3)：310-313,354.
[229]王志刚,李佳,陈令坤.高炉过程多智能体控制系统的开发[J].炼铁,2003,22(3)：51-53.
[230]刘金琨,王树青.钢铁工业过程多智能体系统研究[J].信息与控制,1998,27(6)：427-432.
[231]佘国林.基于Multi-Agent的连铸热轧一体化生产调度系统[D].大连：大连理工大学硕士论文,2007.
[232]方向明.基于Agent的决策支持系统的研究与应用[D].大连：大连交通大学硕士论文,2007.
[233]纪日耿.基于多Agent与极值优化的钢铁集成调度[D].浙江：浙江大学硕士论文,2010.
[234]周建男.钢铁生产工艺装备新技术[M].北京：冶金工业出版社,2004.
[235]郭齐胜,董志明,李亮等.系统建模与仿真(上)[M].北京：国防工业出版社,2007.
[236]田芳,李立新.一种基于熟人关系的Agent联合算法[J].西南师范大学学报(自然科学版),2006,31(1)：108-111.
[237]X.B Wei, R Unland.An XML-Based Agent Communication Framework [C]. In:Workshop On Agents And CSCW:A Fruitful Marriage. The German Conference On Computer—Supported Cooperative Work. Munich. Germany, 2000.
[238]魏晓斌,周盛宗,B Bachmend等.Agent通信机制探讨[J].计算机工程,2002,38(15)：66-70.
[239]D. D Corkill, K. Q Gallagher, K. E Murray. GBB:A generic blackboard development system. In Proceedings of the National Conference on Artificial Intelligence, Philadelphia, Pennsylvania,1986,1008-1014.
[240]王世进,周炳海,陶丽华,等.Agent通讯语言综述[J].计算机工程与应用,2005,(21)：34-37.
[241]R. S Patil,R. E Fikes, P. F Patel-Schneider, et al. The DARPA Knowledge Sharing Effort:Progress report[A].In proceedings of Knowledge Representation and Reasoning,1992,777-788.
[242]J Mayf ield, Y Labrou, T Finin. Evaluating KQML as an agent communication Language[J]. Intelligent Agents Ⅱ Agent Theories,1996, (1037): 347-360.
[243]Welcome to the Foundation for Intelligent Physical Agent. http://www.fipa. org/.2011-4-14.
[244]曹海军,张和明,熊光楞AOSE中Agent行为的形式化与描述方法[J].系统工程与电子技术,2004,26(11)：1684-1686,1693.
[245]盛万兴,杨旭升.多Agent系统及其在电力系统中的应用[M].北京：中国电力出版社,2007.
[246]M.N Huhns, L. M Stephens.Multi-Agent systems and society of Agents[C]. In:Multi-Agent System. Weiss G. Massachusetts, MA:The MIT Press,1999:79-120.
[247]李军,张卓.多Agent系统中Agent的协作机理与模式[J].青岛大学学报(工程技术版).2005,20(3)：43-47.
[248]史美林,向勇,杨光信.计算机支持的协同工作理论与应用[M].北京：电子工业出版社,2002,12.
[249]R Konig. State-based modeling method for multiagent conversation protocols and decision activities[A]. Proceedings of Agent Technologies, Infrastructures, Tools, and Applications for E-Services[C].Erfurt:Springer-Verlag, Lecture Notes in Artificial Intelligence,2003,2592:151-166.
[250]M Nowostawski, M Purvis, S Cranefield. Modelling and visualising agent conversations[A]. Proceedings of the 5th International Conference on Autonomous Agents[C]. Montreal, Canada:ACM Press,2001,163-170.
[251]H. V. D PARUNAK. Visualizing agent conversations:using enhanced dooley graphs for agent design and analysis[A]. Proceedings of the 2nd International Conference on Multi-Agent Systems(ICMAS'96) [C]. Kyoto, Japan:AAAI Press,1996,275-282.
[252]J ODELL, H. V. D PARUNAK, B BAUER. Representing agent interaction protocols in UML[A]. Proceedings of the 1st International Workshop on Agent Oriented Software Engineering[c]. Berlin, Germany:Springer —Verlag,2001,121-140.
[253]焦文品,史忠植.形式化多主体系统中的交互及交互协议[J].软件学报,2001,12(8)：1177-1182.
[254]R. S COST, Y CHEN, T FININ, et al. Using colored Petri nets for conversation modeling[EB/OL]. http://www.csee.umbc.edu/ jklabrou/publications,2004-05-20.
[255]于振华,蔡远利,韩九强.基于面向对象Petri网的多Agent系统交互协议建模[J].计算机集成制造系统,2005,11(8)：1064-1068.
[256]徐乐.基于元胞自动机的钢厂车间天车调度仿真方法研究[D].重庆：重庆大学硕士论文,2007.
[257]A. D Scott, F. W Mark, H. S Clint. Multi-agent Systems Engineering[J]. International Journal of Software Engineering and Knowledge Engineeting,2001,11(3):231-258.
[258]AgentTool3. http://agenttool.cis.ksu.edu/.2011-4-14.
[259]黎建兴,毛新军,束尧.软件Agent的一种面向对象设计模型[J].软件学报.2007,18(3)：582-591.
[260]冯允成,邹志红,周泓.离散系统仿真.北京：机械工业出版社.1998,12.
[261]刘文涛,张群.基于OPT的炼钢连铸生产调度问题研究[J].冶金能源,2004,23(2)：3-6.
[262]杨世强,张海峰,李德信.基于Petri网的FMS物流系统建模与仿真[J].计算机工程与应用,2008,44(22)：226-229.
[263]何腊梅.基于细胞自动机炼钢生产物流仿真调度优化方法研究[D].重庆：重庆大学博士论文,2006.
[264]吕冬瑞,田乃媛,徐安军等.宣钢取消混铁炉可行性分析[J].钢铁研究,2006,34(5)：33-35.
[265]李文兵,纪扬,李华德.基于面向对象的连铸生产过程仿真建模和实现[J].系统仿真学报,2007,19(18)：4288-4291.
[266]刘小华,钟约先,时晓明等.CIMS环境下物流系统面向对象描述和数据库设计[J].机械工业自动化,1998,20(3)：17-20,23.
[2]殷瑞钰.冶金流程工程学[M].北京：冶金工业出版社,2004.
[3]百度百科.http://baike.baidu.com/view/909679. htm.2011-4-14.
[4]吴清一,王转.物流系统工程[M].北京：高等教育出版社,2004.
[5]殷瑞钰.钢铁制造过程的多维物流控制系统[J].金属学报1997,33(1)：29-38.
[6]杨辉,张长森,曹枫等.钢铁企业生产物流合理化研究[J].商业时代,2009(23)：104-105.
[7]陈森发.复杂系统建模理论与方法[M].南京：东南大学出版社,2005.
[8]宋学锋.复杂性、复杂系统与复杂性科学[J].中国科学基金,2003,(5)：262-269.
[9]郝柏林.分岔、混沌、奇怪吸引子、湍流及其它[J].物理学进展,1983,3(3)：340-398
[10]钱学森,于景元.一个科学领域-开放的复杂巨系统及其方法[J].自然杂志,1990,13(1)：3-10.
[11]方美琪,张树人.复杂系统建模与仿真[M].北京：中国人民大学出版社,2002.
[12]迟延,谭跃进,邓宏钟.基于CGP模型的涌现研究[J].系统工程与电子技术,2002,24(1)：10-13.
[13]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3)：416-420.
[14]廖守亿,戴金海.基于Agent的分布仿真中的通信系统设计与实现[J].系统仿真学报,2006,14(2)：1006-1009,1014.
[15]邹吴飞,夏国平,杨方廷.基于自组织算法的改进型GAANN预测模型[J].中国管理科学,2005,13(6)：75-80.
[16]刘晓平,唐益明,郑利平.复杂系统与复杂系统仿真研究综述[J].系统仿真学报,2008,20(23)：6303-6315.
[17]吴哲辉.Petri网导论[M].北京：机械工业出版社,2006.
[18]K Jensen. Colored Petri Nets:Basic Concept, Analysis Methods And Practical Use[M]. Berlin:Spring-Verlag,1997.
[19]唐达,刘丹妮.基于层次时间Petri网的工作流模型可达性分析方法[J].计算机集成制造系统,2006,12(4)：487-493.
[20]刘雪艳,吴慧欣,张强等.基于面向对象时间Petri网的密码协议分析[J].计算机工程,2009,35(13)：156-159.
[21]瞿继双,戴金海.复杂离散实时系统任务／资源图建模方法研究[J].系统仿真学报,2000,12(6)：600-603.
[22]董明,查建中,杜玉明等.用面向对象的框架语言实现离散事件仿真[J].天津大学学报,1997,30(1)：43-48.
[23]B. P Zeigler. Multifaceted modeling and discrete event simulation [M].London and Orando,FL, USA:Academic Press,1984.
[24]P. G Hawkins, D. J Woollons. Failure modes and effects analysis of complex engineering systems using functional models[J]. Artificial Intelligence in Engineering(S0954-1810),1998,12(4):375-397.
[25]葛建新,杨莉.基于整数规划的几何因果定性推理建模和求解[J].计算机学报,1997,20(12)：1095-1104.
[26]顾宇红,石纯一.基于分层因果关系的定性推理方法[J].软件学报,1998,9(12)：884-888.
[27]Y. Q Zhao, D. F Zhu, H Wang et al.Modeling and Simulating for the Manufacturing Process of Metallurgy based on System Dynamics.2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics,2010, Wuhan,105-109.
[28]赵业清,王华.区域钢铁产品生命周期物质流分析—基于系统动力学方法的建模与仿真.工业加热,2009,38(1)：9-13.
[29]朱大智,吴俊,谭跃进等.度秩函数：一个新的复杂网络统计特征[J].复杂系统与复杂性科学,2006,3(4)：28-34.
[30]张嗣瀛.复杂系统、复杂网络自相似结构的涌现规律[J].复杂系统与复杂性科学,2006,3(4)：41-51.
[31]S Boccaletti,V Latora, Y Moreno, et al. Complex Networks:Structure and Dynamics[J]. Complex Systems and Complexity Science,2006,3(3):56-94.
[32]S Boccaletti, V Latora, Y Moreno, et al. Complex Networks:Structure and Dynamics[J]. Complex Systems and Complexity Science,2006,3(4):52-90.
[33]毛嫒,刘杰,李伯虎.基于元模型的复杂系统建模方法研究[J].系统仿真学报,2002,14(4)：411-414,454.
[34]康卓,黄竞伟,李艳等.复杂系统数据挖掘的多尺度混合算法[J].软件学报,2003,14(7)：1229-1237.
[35]胡晓峰.战争复杂性与信息化战争模拟[J].系统仿真学报,2006,18(12)：3572-3580.
[36]崔霞,戴汝为,李耀东.群体智慧在综合集成研讨厅体系中的涌现.系统仿真学报,2003,15(1)：146-153.
[37]E Kondili,C Pantelidis, R Sargent. A General Algorithm for Short-term Scheduling of Batch Operations-I. MILP Formulation[J]. Computer & Chemical Engineering,1993,17(22):211-227.
[38]V.T Voudouris, I.E Grossmann. MILP Model for Scheduling and Design of a Special Class of Multipurpose Batch Plants [J]. Computer & Chemical Engineering,1996,20(11):1335-1360.
[39]C. A Mendez, J Cerda. An Efficient MILP Continuous-time Formulation for Short-term Scheduling of Multiproduct Continuous Facilities [J]. Computer & Chemical Engineering,2002,26(4-5):687-695.
[40]C. A Mendez, J Cerda. Dynamic Scheduling in Multi-product Batch Plants[J]. Computer & Chemical Engineering,2003,27(8-9):1247-1259.
[41]G Schilling, C. C Pantelides. A Simple Continuous-time Process Scheduling Formulation and A Novel Solution Algorithm[J]. Computer & Chemical Engineering,1996,20(supp2):1221-1226.
[42]M Yuceer, R Berber. A Heuristic Approach for Solution of MINLP Problem for Production Scheduling in Preemptive Mode[J]. Chemical Engineering &Processing,2005,44(9):933-940.
[43]C. N Potts, V. L Wassenhove.A Branch and Bound Algorithm for the Total Weighted Tardiness Problem [J]. Operation Reseach,1985,33 (3):367-377.
[44]刘晓霞.基于遗传分枝定界算法的Flow-shop调度问题[J].控制工程,2003,10(3)：205-208,232.
[45]M. W Park, Y. D Kim. A Branch and Bound Algorithm for a Production Scheduling Problem in an Assembly System under Due Date Constraints [J]. European Journal of Operational Research,2000,123(3):504-518.
[46]G Rabadia, M Mollaghasemib, G.C Anagnostopoulos. A Branch-and-Bound Algorithm for the Early/Tardy Machine Scheduling Problem with a Common Due-date and Sequence-dependent Setup Time[J]. Computers & Operations Research,2004,31(10):1727-1751.
[47]熊锐,张俊杰,卢改林.用拉氏松弛法解决FMS中一类特殊的生产调度问题[J].西安工业学院学报,1997,17(4)：272-277.
[48]熊锐,陈浩勋,胡保生.一种生产计划与车间调度的集成模型及其拉氏松弛求解法[J].西安电子科技大学学报,1996,23(4)：509-516.
[49]L. X Tang, B.P Luh, J. Y Liu et al. Steel-making Process Scheduling Using Lagrangian Relaxation[J]. International Journal of Production Research,2002,40(1):55-70.
[50]张凤梅,洪运国.基于多机调度问题的动态规划算法[J].计算机技术与发展,2006,16(3)：61-62,65.
[51]H Park, Y Hong, S. Y Chang. An Efficient Scheduling Algorithm for the Hot Coil Making in the Steel Mini-mill[J]. Production Planning & Contol, 2002,13, (3):298-306.
[52]黄可为,卢克斌,汪定伟.炼钢组炉问题优化模型及其动态规划算法[J].东北大学学报(自然科学版),2006,27(2)：138-141.
[53]吴锦标,刘再明,尹小玲等.基于排队论的一个物流模型[J].系统工程理论与实践,2009,29(9)：78-83.
[54]吴耀华,王胜力,王常香.排队论在物流规划中的应用[J].物流科技,2004,27(105)：53-55.
[55]Y. W Kim, T Suzuki, T Narikiyo. FMS scheduling based on timed Petri Net model and reactive graph research [J]. Applied Mathematical Modelling, 2007,31(6):955-970.
[56]W. M Zuberek, W Kubiak. Timed Petri Nets in Modeling and Analysis of Simple Schedules for Manufacturing Cells[J]. Computers and Mathematics with Applications,1999,37(11-12):191-206.
[57]李霄峰,徐立云,邵惠鹤等.炼钢连铸系统的动态调度模型和启发式调度算法[J].上海交通大学学报,2001,35(11)：1658-1662.
[58]嵇振平,陈文明,于戈.分层有色Petri Net(HCPN)及其在宝钢炼钢连铸生产物流系统仿真建模中的应用[J].冶金自动化,2002,(2)：6-9.
[59]昊亚丽,曾建潮,卫军胡,等.一种基于混合Petri网的批过程动态调度算法[J].西安交通大学学报,2002,36(2)：147-151.
[60]何腊梅,郑忠,高小强.基于时间倒推和流程仿真炼钢生产计划的制定[J].重庆大学学报(自然科学版),2006,29(9)：60-63.
[61]何腊梅.炼钢生产物流的细胞自动机仿真模型研究[D].重庆：重庆大学硕士论文,2003.
[62]郑忠,何腊梅,高小强.基于细胞自动机的流程仿真方法在炼钢生产中的应用[A].2005年中国钢铁年会论文集[C],冶金工业出版社,北京,2005,718-723.
[63]李陶深.人工智能[M].重庆：重庆大学出版社,2002.
[64]D. F Guo, T. K Li. Rescheduling Algorithm for Steelmaking-Continuous Casting[A].2007 Second IEEE Conference on Industrial Electronics and Applications[C],2007,1421-1425.
[65]刘光航,李铁克.炼钢-连铸生产调度模型及启发式算法[J].系统工程,2002,20(6)：44-48.
[66]郝文育,李亚白,王宁生.一种启发式车间作业调度算法的研究与应用[J].机械科学与技术,2005,24(7)：861-864.
[67]张德富,李新.求解作业车间调度问题的快速启发式算法[J].计算机集成制造系统,2005,11(2)：237-241.
[68]S.S Panwalkar. A survey of Scheduling Rules[J], Operations Research, 1977,25(1):45-61.
[69]林海凡,李东波,唐敦兵等.生产调度问题的启发式遗产算法[J].机械科学与技术,2000,19(5)：790-792.
[70]唐立新,杨自厚,王梦光等.CIMS中带多资源的CLSP问题的遗传启发式算法[J],系统工程理论与实践,1997,(4)：39-44.
[71]冯振军,杨根科,杜斌等.炼钢连铸调度的启发式和线性规划两步优化算法[J],冶金自动化,2005,(4)：18-22.
[72]K. S Metaxiotis, D Askounis, J Psarras. Expert Systems in Production Planning and Scheduling:A State-of-the-art Survey[J].Journal of Intelligent Manufacturing,2002,13(4):253-260.
[73]樊俊飞,李永如,姚海石.宝钢转炉吹炼控制模拟在线专家系统[J].宝钢技术,1996,(4)：50-54,58.
[74]龚宇,熊光楞.机器学习在智能车间调度系统中的应用[J].控制与决策,1997,12(3)：222-227,233.
[75]李蓓智,杨建国,丁惠敏.基于生物免疫机理的智能调度系统建模与仿真[J].计算机集成制造系统,2002,8(6)：446450.
[76]刘研,张俐丽,刘涛.遗传算法在车间作业调度中设计与应用[J].自动化技术与应用,2009,28(12)：8-12.
[77]李铁克,王伟玲,张文学.基于文化遗传算法求解柔性作业车间调度问题[J].计算机集成制造系统,2010,16(4)：861-866.
[78]梁迪,谢里阳,隋天中等.基于遗传和禁忌搜索算法求解车间调度优化问题[J].计算机应用,2006,26(4)：857-860.
[79]邓泽林,黄文奇,周立刚.求解车间作业调度问题的快速禁忌搜索算法[J].华中科技大学学报(自然科学版),2003,31(11)：1-3.
[80]黄明,闰淑娟,梁旭.遗传算法和禁忌搜索算法在车问调度中的研究进展.工业控制计算机,2004,17(2)：4-5.
[81]杨玉文,王俊茹,张宏芳.求解Flow shop调度问题的混合禁忌搜索算法[J].哈尔滨师范大学自然科学学报,2007,23(3)：59-61.
[82]潘全科,段俊华,赵清理,朱剑英.解决车间调度问题的改进模拟退火算法[J].机械科学与技术,2007,26(1)：112-114.
[83]吴大为,陆涛栋,刘晓冰等.求解作业车间调度问题的并行模拟退火算法[J].计算机集成制造系统,2005,11(6)：847-850
[84]薛拾贝,席裕庚.用蚁群算法求解Job-Shop问题的机器分解方法[J].计算机仿真,2008,25(11)：187-190,225.
[85]张维存,郑丕谔,吴晓丹.蚁群遗传算法求解能力约束的柔性作业车间调度问题[J].计算机集成制造系统,2007,13(2)：333-337,362.
[86]Y. Q Yun, J. G Sun, J. Y Zhang et al. A hybrid discrete particle swarm algorithm for open shop problems[C]. Proceedings of the 6th International Conference on Simulated Evolution and Learning(SEAL 2006). Hefei, China,2006:158—165.
[87]张长胜,孙杰贵,欧阳丹彤等.求解车间调度问题的自适应混合粒子群算法[J].计算机学报,2009,32(11)：2137-2146.
[88]贾兆红,陈华平,孙耀晖.混合粒子群算法在柔性工作车间调度中的应用[J].系统仿真学报.2007,19(20)：4743-4747.
[89]S Yang, D. A Wang. A New Adaptive Neural Network and Heuristics Hybrid Approach for Job-Shop Schedul ing [J]. Computers & Operations Research, 2001,28(10):955-971.
[90]王万良,吴启迪,徐新黎.基于Hopfield神经网络的作业车间生产调度方法[J].自动化学报,2002,28(5)：838-844.
[91]徐新黎,王万良,吴启迪.改进计算能量函数下作业车间调度的混沌神经网络方法[J].控制理论与应用,2004,21(2)：311-315.
[92]马鑫,梁艳春.基于GPGP协同机制的多Agent车间调度方法研究[J].计算机研究与发展,2008,45(3)：479-486.
[93]牟文恒,吕志民,唐荻.多代理机制在炼钢连铸排程中的应用研究[J].钢铁,2006,41(5)：2932.
[94]宋娟.基于CNP的多Agent车问调度系统设计与实现[J].工业控制计算机,2010,23(6)：82-84.
[95]李应,杨善林,左春荣.敏捷制造系统的多Agent结构与调度方法研究[J].合肥工业大学学报(自然科学版),2005,28(5)：541-545.
[96]曾齐红.基于遗传算法求解炼钢—连铸浇次排序问题[J].鞍山师范学院学报,2004,6(6)：70—73.
[97]崔志华,高慧敏,曾建潮.一种求解连铸生产调度问题的非线性遗传算法[J].计算机工程与应用,2004,(35)：205-207.
[98]M Liu, C Wu. Identical Parallel Machine Scheduling Problem for Minimizing the Makespan Using Genetic Algorithm Combined by Simulated Annealing [J].Chinese Journal of Electronics.1998,7(10):317-3321.
[99]陈璐,奚立峰,蔡建国,等.一种求解带有阻塞限制的混合流水车间的禁忌搜索算法[J].上海交通大学学报,2006,40(5)：854-857.
[100]朱道飞.钢厂生产作业计划与调度的自组织优化方法研究[D].重庆：重庆大学博士论文,2008.
[101]郑忠,朱道飞,高小强.基于蚁群算法的炼钢-连铸作业计划编制方法.北京科技大学学报,2009,31(4)：504-510.
[102]朱道飞,郑忠,高小强.炼钢-连铸作业计划的遗传优化模型.钢铁,2008,43(7)：26-31.
[103]ZHU Dao-fei,WANG Hua, WANG Shi-bo et al. Hybrid GeneticAlgorithm-based Production Planning for Steel-making and Continuous Casting Process[C]. ICEEAC2010, V2,484-488.
[104]朱道飞,王华,马长波.准时制下炼钢生产计划时间并行顺推编制方法.计算机工程与应用,2010,46(32)：236-240.
[105]郑忠,朱道飞,高小强.混合流程作业计划编制的时间并行倒推算法.计算机集成制造系统,2008,14(4)：749-756
[106]郑忠,朱道飞,高小强.钢厂炼钢-连铸生产调度及重计划方法.重庆大学学报(自然科学版),2008,31(7)：820-824.
[107]ZHU Dao-fei, ZHENG Zhong, GAO Xiao-qiang. Intelligent Optimization Based Production Planning and Simulation Analysis for Steel making and Continuous Casting Process.Journal of IRON AND STEEL RESEARCH, INTERNATIONAL.2010,17 (9):19-24,30.
[108]Visual8 Simulation Modeling & Decision Support Solutions. http://www. visual8. com/v8s. html.2011-4-14.
[109JSIMATIC IT. http://www.ca800.com/product/html/2007-6-28/p101982. html.2011-4-14.
[110]PSI Metals. http://www.steelplanner.com.2011-4-14.
[111]IBM ILOG Optimization. http://www-01.ibm.com/software/websphere/ products/optimization/.2011-4-14.
[112]Gensym|Business Rule Management. http://www.gensym.com.2011-4-14.
[113]S. C Hamoen, D. J Moens.Logistic Simulator for Steel Producting Factories[C]. Proceedings of the 2002 Winter Simulation Conference. 2002,1315-1318.
[114]常春光,胡琨元,汪定伟等.钢铁生产动态调度理论研究与工程应用综述[J].信息与控制,2003,32(6)：531-537.
[115]M Numao, S Morishita. Scheplan-A Scheduling Expert for Steel-Making Process[A]. Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications[C], Hitachi,1988,467-472.
[116]K. R Ryu,0. K Lim, H. K Lee. The Advanced Production and Product Scheduling Control System at the Kwangyang Works[C]. Proceedings of International Conference on Computerized Production Control in Steel Plant.South Korea:Posdata Corp,1993,299-308.
[117]K Stohl, W Snopek. VAI-Schedex:A Hybrid Expert System for Cooperative Production Scheduling in a Steel Plant[C]. Proceedings of International Conference on Computerized Production Control in Steel Plant.South Korea:Korea Press.1993,207-217.
[118]杨越.钢铁厂综合化生产调度系统[J].冶金自动化,1998,(5)：22-25,35.
[119]A Stephens. Integrated Scheduling of Steel Production[J]. Asia Steel, 1998:150-153.
[120]徐安军,田乃媛.薄板坯连铸-连轧流程的物流参数分析[J].钢铁研究学报,1998,10(4)：13-16.
[121]邱剑,田乃媛,徐安军等.宝钢铁钢界面运输调度应用程序的开发[J].钢铁,2003,38(5)：73-76.
[122]刘青,田乃媛,殷瑞钰.炼钢厂的运行控制[J].钢铁,2003,38(9)：14-18.
[123]汪开忠,田乃媛.马钢一钢厂三炉对四机生产调度模型研究[J].中国冶金,2005,(6)：29-33.
[124]王佳,田乃媛,徐安军等.新一代大型钢厂生产流程的计算机仿真系统[J].冶金自动化,2007,31(3)：31-35.
[125]徐安军,田乃媛,吴晓东等.宝钢炼钢厂生产调度模拟系统的初步讨论[J].钢铁,1999,3(3)：1-7.
[126]徐幸天,欧阳昭,田乃媛等.全连铸生产计算机调度管理系统[J].钢铁,1998,33(4)：16-19.
[127]高振,唐立新.钢铁企业物流研究概述[J].控制与决策,2001,16(1)：12-15.
[128]李英锦,唐立新,王梦光.基于可视化虚拟现实技术的炼钢-连铸生产调度系统[J].冶金自动化,1999,(4)：14-17,49.
[129]李霄峰,徐立云,邵惠鹤等.柔性炼钢连铸仿真调度系统及其关键技术[J].系统仿真学报,2002,14(2)：207-210,252.
[130]孙羽中,邵惠鹤.物流仿真器在宝钢生产应用中的研究[J].自动化仪表,1999,20(9)：6-8.
[131]常俊林,邵惠鹤.两机零等待流水车间调度问题的启发式算法[J].计算机集成制造系统,2005,11(8)：1147-1153,1162.
[132]李霄峰,徐立云,邵惠鹤等.反向仿真以及在JIT生产调度中的应用[J].系统仿真学报,2001,13(S1)：146-148.
[133]孙福权,唐立新,郑秉霖.炼钢-连铸生产调度专家系统[J].冶金自动化,1998,(6)：31-33.
[134]郑忠,何腊梅,高小强.炼钢生产物流系统仿真的细胞自动机模型[J].钢铁,2004,39(11)：75-78.
[135]刘晓冰,王顺宝,蒙秋男等.钢铁企业集成化生产调度模型的研究[J].微处理机,2007,28(2)：83-86.
[136]薄洪光,刘晓冰,吕艳霞.钢铁企业xBOM过程集成研究[J].工业工程与管理,2009,14(2)：76-80.
[137]薄洪光.钢铁企业集成生产物流管理方法及应用研究[D].大连：大连理工大学博士论文,2008.
[138]马跃,徐然,刘晓冰.钢铁企业先进生产计划和实时生产调度技术的研究[J].制造业自动化,2003,25(3)：35-39.
[139]李宏,李苏剑.武钢第二热轧厂生产物流管理系统[J].物流技术与应 用,2004,(11)：86-88.
[140]李蒙蒙,李苏剑,王冰洁.武钢二热轧生产物流管理信息系统软件设计与描述[J].物流科技,2004,27(112)：75-77.
[141]王冰洁,李苏剑.武钢二热轧板坯库实时生产物流管理信息系统研究与设计[J].物流科技,2004,(12)：61-63.
[142]何志林,李苏剑.武钢2250mm热轧厂生产物流管理系统的研究与开发[J].物流技术,2004,(2)：69-71.
[143]崔建江,罗首章,刘峰等.铁水运输仿真系统的设计与实现[J].系统仿真学报,2003,15(12)：1799-1802.
[144]沈益军.基于SCOR模型的宝钢出厂物流体系分析与改进[D].上海：上海交通大学硕士论文,2008.
[145]沈益军.宝钢出厂物流的SCOR模型分析[J].物流技术,2007,30(6)：65-68.
[146]周忠志,蒋琼珠.基于GPSS-F仿真语言的港口物流仿真自动建模系统[J].交通与计算机,1994,(5)：11-15.
[147]冯新宇,黄凤英.基于GPSS/H和GKS的FMS物流系统的动画仿真[J].计算机工程,1993,19(6)：38-42.
[148]潘燕春,周泓,冯允成.基于Arena的车间作业排序问题建模方法及其仿真优化系统设计[J].计算机集成制造系统,2006,12(3)：389-394.
[149]E Oliveira, K Fischer,0 Stepankova. Multi-agent Systems:Which Research for Which Applications[J]. Robotics and Autonomous Systems, 1998,7(1-2):91-106.
[150]J. Ferber. Multi-agent Systems:An Introduction to Distributed Artificial Intelligence[M]. Addision Wesley, London,1999.
[151]H. S Nwana, L. C Lee, N. R Jennings. Coordination in Software Agent Systems[J]. The British Telecon Technical Journal,1996,14(4):79-88.
[152]D Ouelhadj. Etat de 1'art sur les systemes multi-agents[R]. In:Writing Master report,1996.
[153]G Weiss. Multi-agent Systems:A Modern Approach to Distributed Artificial Intelligence.The MIT Press, Cambridge, Massachusetts, London,England,1999.
[154]张维明,姚莉.智能协作信息技术[M].北京：电子工业出版社,2002.
[155]M Wooldridge. An Introduction to Multi-Agent Systems[M]. Chichester: John Wiley & Sons,2002.
[156]F Zambonelli, A Omicini. Challenges and research directions in agent-oriented software engineering[J]. Autonomous Agents and Multi-Agent Systems,2004,9(3):253-283
[157]J Ferber, M Ghallab. Problematique des univers multi-agents. In:Actes des Journees Nationals du PRC-IA,Cepadus-edition, Toulouse,France, 1988.
[158]P Maes.Artificial Life Meets Entertainment:Life like Autonomous Agents.Communicaion of the ACM,1995,38(11):108-114.
[159]M Wooldridge,N.R Jennings. Intelligent Agents:Theory and Practice. Knowledge Engineering Review,1995,10(2):115-152.
[160]L.P Kaelbing.A Situated Automata Approach to the Design of Embedded Agents[J].Sigart Bulletin,1991,2(4):85—88.
[161]M.E Bratman. Intention, Plans, and Practical Reason [M].Harvad University Press, Cabridge, MA,1987.
[162]A.S Rao, M. P Georgeff. Formal models and decision procedures for Multi-Agent systems[M]. Technical note 61, Australian AI Institute, level 6,171 La Trobe Stree, Melbourne, Australia,1995.
[163]R. A Brooks. Elephants don't play chess[M].In Designing Autonomous Agents,3-15. MIT Press, Cambridge, MA,1990.
[164]M.P Georgeff,A. L Lansky. A System for Reasonning in Dynamic Domains:Fault Diagnosis on the Space Shuttle.Technical Note 375, Artificial Intelligience Center, SRI International, Menlo Park,California,1986.
[165]范辉,李晋江,张晖Agent技术的研究现状与发展趋势[J].微型机与应用,2001,(10)：4-7.
[166]E Durfee,V R Lesser, D.D Corkill. Trends in cooperative distributed problem solving[C]. IEEE Transactions on Knowledge and Data Engineering,1989,1(1):63-83.
[167]J Ferber. Les systemes multi-agents:Apercu general[J]. Techniques et Sciences Informatiques,1997,16(8):979-1012.
[168]E Oliveira, K Fischer,0 Stepankova. Multi-agent systems:which research for which applications [J]. Robotics and Autonomous Systems, 1998,7(1-2):91-106.
[169]N.R Jennings,K Sycara, M Wooldridge. A roadmap of agent research and development[J]. Autonomous Agents and Multi-Agent Systems,1998, 1(1):7-38.
[170]石纯一,张伟,徐晋晖等译.多Agent系统引论[M].北京：电子工业出版社,2003.
[171]M. R Genesereth, S. P Ketchet.Software Agent[J].Communication of the ACM,1993,37(7):48-53.
[172]赵龙文,侯义斌.多Agent系统及其组织结构[J].计算机应用研究,2000(7)：12-15.
[173]王德军,郝永芳.结构化程序设计方法与面向对象的程序设计方法的比较[J].铁路计算机应用,2003,12(11)：13-15.
[174]刁成嘉.面向对象技术导论-系统分析与设计[M].北京：机械工业出版社,2007.
[175]符于江.程序设计中结构化方法和面向对象方法的比较[J].电脑知识与技术,2008(21)：451—452.
[176]姚郑,高文.面向Agent的程序设计[J].软件学报,1997,8(11)：824-831.
[177]P Ciancarini, M Wooldridge. Agent-oriented software engineering [C]. In:Proc. of Intl. Conf. on Software Engineering(ICSE-2000). Los Alamitos.2000,816-817
[178]张纪海.国民经济动员系统建模与仿真研究[M].北京：北京理工大学出版 社,2008.
[179]江志斌Petri网及其在制造系统建模与控制中的应用[M].北京：机械工业出版社,2004.
[180]赵业清,朱道飞,王华等.基于分层时延CPN的轧钢系统建模[J].计算机工程,2011,37(1)：229-231.
[181]Y. Q Zhao, D. F Zhu, H Wang, et al. A Multi-Agent Systems Model for Rolling System Based on Petri Nets.2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics.2010, Wuhan,101-104.
[182]赵业清,朱道飞,王华等.基于Petri网和Agent复杂适应系统建模[J].计算机工程,2011第16期.
[183]宗大华,宗涛.操作系统[M].北京：人民邮电出版社,2006.
[184]C Ramchandani. Analysis of Asynchronous Concurrent Systems by Timed Petri Nets. Project MAC, TR120, M. I. T.,1974.
[185]K Garg. An Approach to Performance Specification of Communication Protocols Using Timed Petri Nets[A]. IEEE Transaction on Software Engineering,1985,11(10):1216-1225.
[186]M. K Melloy. Performance Analysis Using Stochastic Petri Nets [A]. IEEE Transactions on Computers,1982,31 (9):913-917.
[187]P. J Haas, G. S Shedler. Stochastics Petri Net Reparesentation of Discrete Envent Simulations[A]. IEEE, Transaction on software Engineering,1989,15(4):381-393.
[188]K Jenson. Coloured Petri Nets and the invariant method[J]. Theoretical Computer Science,1981, (14):317-336.
[189]J. L Peterson. Petri Net Theory and the Modeling of Systems[M]. Prentice-Hall,1981.
[190]A Camurri, P Franchi, M Vitale. Object-Oriented Approach to High-Level Petri Nets[J]. Microprocessing and Microprogramming,1992, (35):213-220.
[191]L.C Wang. Object-oriented Petri nets for modelling and analysis of automated manufacturing systems[J]. Computer Integrated Manufacturing Systems,1996,9(2):111-125.
[192]Z Jiang, M. J Zuo, P. Y Tu, et al. Object-Oriented Petri Nets with Changeable Structure (OPNs-CS) for Production System Modelling[J]. Int J Adv Manuf Technol,1999, (15):445-458.
[193]薛霄,贾宗璞.面向Agent的软件设计与开发方法[M].北京：电子工业出版社,2009.
[194]AUML Web Site. http://auml.org/.2011-4-14.
[195]M Kunstic, M Bagic. Verification and Validation of Multiagent System Specified by Agent UML[C]. Proceeding of 1st International Conference on Information & Communication Technologies from Theory to Applications 2004,842-853.
[196]SwarmWiki. http://www. swarm.org.2011-4-14.
[197]Repast Suite. http://repast.sourceforge. net.2011-4-14.
[198]Brookings. http://www. brook.edu/es/dynamics/models/ascape/default. htm.2011-4-14.
[199]Netlogo Home Page. http://ccl.northwestern.cdu/netlogo/.2011-4-14.
[200]Starlogo on the Web. http://education.mit.cdu/starlogo/.2011-4-14.
[201]XJ Technologies, http://www.AnyLogic.cn/.2011-4-14.
[202]IRIT-UMR. http://www.irit.fr/cosi/training/evaluationoftools/Evalu ation-of-simulation-tools.htm.2011-4-14.
[203]Software for Agent-based Computational Economics and CAS. http://www.econ.iastate.cdu/tesfatsi/acecode. htm.2011-4-14.
[204]M Veloso, P Stone, K Han. The CMUnited-97 robotic soccer team: Perception and multi-agent control[J]. Robotics and Autonomous Systems,1999, (29):133-143.
[205]D. M Lane, A. C Mcfadzean.Distributed problem solving and realtime mechanisms in robot architecture[J].Engineering Application Intelligence,1994,7(2):105-117.
[206]Y. E Soo,T.S Chang, D Shin et al. Cooperative problem solving in diagnostic agents for chemical processes[J]. Computers and Chemical Engineering,2000, (24):729-734.
[207]T Oyabu, T Misawa, H Kimura,et al. Proposition of sensor agent for estimation of air-pollution direction and its experimental simulation[J]. Materials Science and Engineering,2000,12:89-95.
[208]吴泉源,王怀民.银河分布计算环境[J].软件学报,1997,8(10)：745-751.
[209]T. P Liang, J. S Huang. A framework for applying intelligent agents to support electronic trading[J]. Decision Support Systems,2000, 28:305-317.
[210]K. J Lee, Y.S Chang, J. K Lee. Time-bound negotiation framework for electronic commerce agents[J]. Decision Support Systems,2000, 28:319-331.
[211]J. J Jung, G. S Jo. Brokerage between buyer and seller agents using Constrain Satisfaction Problem models[J]. Decision Support Systems, 2000, (29):293-304.
[212]A Sluga, P Butala, G Bervar. A multi-agent approach to process planning and fabrication in distributed manufacturing[J]. Computers & Industrial Engineering,1998, (35):455-458.
[213]M Schroeder. Towards a visualization of arguing agents[J]. Future Generation Computer Systems,2000, (17):15-26.
[214]K. M Nelson, A Kogan. Virtual auditing agents:the EDGAR Agent challenge [J]. Decision Support Systems,2000, (28):241-253.
[215]N. R Jennings, L.Z Varge, R. P Aarnts. Transforming standalone expert systems into a community of cooperating agents[J]. Engineering Application Artificial Intelligence,1993,6(4):317-331.
[216]P Maes. Agents that reduce work and information overload[J]. Communications of the ACM,1994,37(7):31-40.
[217]张延华,李鹏辉,刘彦春等.基于多智能体钢板质量评估与改进专家系统模型[J].计算机工程与设计,2008,29(6)：1579-1584.
[218]J. P Wangermann, R.F Stengel. Principled negotiation between intelligent agents:a model for air traffic management [J]. Artificial Intelligence in Engineering,1998, (12):177-187.
[219]G Fakas, B Karakostas. A workflow management system based on intelligent collaborative objects[J]. Information and Software Technology,1999, (41):907-915.
[220]G. Q Huang, J Huang,K. L Mak. Agent-based workflow management in collaborative product development on the Internet[J]. Computer Aided Design.2000, (32):133-144.
[221]Y. H Yan, T Kuphal, J Bode. Application of multiagent systems in project management[J]. Int. J. Production Economics,2000,68:185-197.
[222]金愿华,饶运清.基于Multi-Agent的车间作业调度系统的研究[J].机械与电子,2003,(3)：58-61.
[223]李彪.多智能体冶铸轧调度系统的开发研究[D].呼和浩特：内蒙古科技大学硕士论文,2008.
[224]董洁,尹怡欣,彭开香.流程工业多智能体系统协调控制[J].系统工程理论与实践,2008,(10)：119-124.
[225]秦斌,王欣,吴敏等.MA8在焦炉中的应用[J].钢铁,2005,40(10)：25-29.
[226]秦斌,王欣,吴敏等.基于MAS的分布式棒线材轧制在线生产调度系统[J].钢铁,2005,40(9)：49-53.
[227]陈勇,吴国献,林飞龙.多品种多工艺车间作业调度的Multi-Agent建模[J].浙江大学学报(工学版),2009,43(9)：1672-1678.
[228]陈勇,林飞龙.制造企业物流多智能体建模及其应用[J].浙江工业大学学报,2006,34(3)：310-313,354.
[229]王志刚,李佳,陈令坤.高炉过程多智能体控制系统的开发[J].炼铁,2003,22(3)：51-53.
[230]刘金琨,王树青.钢铁工业过程多智能体系统研究[J].信息与控制,1998,27(6)：427-432.
[231]佘国林.基于Multi-Agent的连铸热轧一体化生产调度系统[D].大连：大连理工大学硕士论文,2007.
[232]方向明.基于Agent的决策支持系统的研究与应用[D].大连：大连交通大学硕士论文,2007.
[233]纪日耿.基于多Agent与极值优化的钢铁集成调度[D].浙江：浙江大学硕士论文,2010.
[234]周建男.钢铁生产工艺装备新技术[M].北京：冶金工业出版社,2004.
[235]郭齐胜,董志明,李亮等.系统建模与仿真(上)[M].北京：国防工业出版社,2007.
[236]田芳,李立新.一种基于熟人关系的Agent联合算法[J].西南师范大学学报(自然科学版),2006,31(1)：108-111.
[237]X.B Wei, R Unland.An XML-Based Agent Communication Framework [C]. In:Workshop On Agents And CSCW:A Fruitful Marriage. The German Conference On Computer—Supported Cooperative Work. Munich. Germany, 2000.
[238]魏晓斌,周盛宗,B Bachmend等.Agent通信机制探讨[J].计算机工程,2002,38(15)：66-70.
[239]D. D Corkill, K. Q Gallagher, K. E Murray. GBB:A generic blackboard development system. In Proceedings of the National Conference on Artificial Intelligence, Philadelphia, Pennsylvania,1986,1008-1014.
[240]王世进,周炳海,陶丽华,等.Agent通讯语言综述[J].计算机工程与应用,2005,(21)：34-37.
[241]R. S Patil,R. E Fikes, P. F Patel-Schneider, et al. The DARPA Knowledge Sharing Effort:Progress report[A].In proceedings of Knowledge Representation and Reasoning,1992,777-788.
[242]J Mayf ield, Y Labrou, T Finin. Evaluating KQML as an agent communication Language[J]. Intelligent Agents Ⅱ Agent Theories,1996, (1037): 347-360.
[243]Welcome to the Foundation for Intelligent Physical Agent. http://www.fipa. org/.2011-4-14.
[244]曹海军,张和明,熊光楞AOSE中Agent行为的形式化与描述方法[J].系统工程与电子技术,2004,26(11)：1684-1686,1693.
[245]盛万兴,杨旭升.多Agent系统及其在电力系统中的应用[M].北京：中国电力出版社,2007.
[246]M.N Huhns, L. M Stephens.Multi-Agent systems and society of Agents[C]. In:Multi-Agent System. Weiss G. Massachusetts, MA:The MIT Press,1999:79-120.
[247]李军,张卓.多Agent系统中Agent的协作机理与模式[J].青岛大学学报(工程技术版).2005,20(3)：43-47.
[248]史美林,向勇,杨光信.计算机支持的协同工作理论与应用[M].北京：电子工业出版社,2002,12.
[249]R Konig. State-based modeling method for multiagent conversation protocols and decision activities[A]. Proceedings of Agent Technologies, Infrastructures, Tools, and Applications for E-Services[C].Erfurt:Springer-Verlag, Lecture Notes in Artificial Intelligence,2003,2592:151-166.
[250]M Nowostawski, M Purvis, S Cranefield. Modelling and visualising agent conversations[A]. Proceedings of the 5th International Conference on Autonomous Agents[C]. Montreal, Canada:ACM Press,2001,163-170.
[251]H. V. D PARUNAK. Visualizing agent conversations:using enhanced dooley graphs for agent design and analysis[A]. Proceedings of the 2nd International Conference on Multi-Agent Systems(ICMAS'96) [C]. Kyoto, Japan:AAAI Press,1996,275-282.
[252]J ODELL, H. V. D PARUNAK, B BAUER. Representing agent interaction protocols in UML[A]. Proceedings of the 1st International Workshop on Agent Oriented Software Engineering[c]. Berlin, Germany:Springer —Verlag,2001,121-140.
[253]焦文品,史忠植.形式化多主体系统中的交互及交互协议[J].软件学报,2001,12(8)：1177-1182.
[254]R. S COST, Y CHEN, T FININ, et al. Using colored Petri nets for conversation modeling[EB/OL]. http://www.csee.umbc.edu/ jklabrou/publications,2004-05-20.
[255]于振华,蔡远利,韩九强.基于面向对象Petri网的多Agent系统交互协议建模[J].计算机集成制造系统,2005,11(8)：1064-1068.
[256]徐乐.基于元胞自动机的钢厂车间天车调度仿真方法研究[D].重庆：重庆大学硕士论文,2007.
[257]A. D Scott, F. W Mark, H. S Clint. Multi-agent Systems Engineering[J]. International Journal of Software Engineering and Knowledge Engineeting,2001,11(3):231-258.
[258]AgentTool3. http://agenttool.cis.ksu.edu/.2011-4-14.
[259]黎建兴,毛新军,束尧.软件Agent的一种面向对象设计模型[J].软件学报.2007,18(3)：582-591.
[260]冯允成,邹志红,周泓.离散系统仿真.北京：机械工业出版社.1998,12.
[261]刘文涛,张群.基于OPT的炼钢连铸生产调度问题研究[J].冶金能源,2004,23(2)：3-6.
[262]杨世强,张海峰,李德信.基于Petri网的FMS物流系统建模与仿真[J].计算机工程与应用,2008,44(22)：226-229.
[263]何腊梅.基于细胞自动机炼钢生产物流仿真调度优化方法研究[D].重庆：重庆大学博士论文,2006.
[264]吕冬瑞,田乃媛,徐安军等.宣钢取消混铁炉可行性分析[J].钢铁研究,2006,34(5)：33-35.
[265]李文兵,纪扬,李华德.基于面向对象的连铸生产过程仿真建模和实现[J].系统仿真学报,2007,19(18)：4288-4291.
[266]刘小华,钟约先,时晓明等.CIMS环境下物流系统面向对象描述和数据库设计[J].机械工业自动化,1998,20(3)：17-20,23.