基于自治与协商机制的柔性制造车间智能调度技术研究
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摘要
柔性制造环境下车间生产调度问题具有复杂性、不确定性、多目标、多约束、多资源相互协调等特点。鉴于其重要的理论和实际意义,一直以来是生产管理和组合优化领域的重点和难点课题。本论文在充分吸收近十余年间基于Agent或Holon的非集中式生产调度最新研究成果基础上,以具有生产加工柔性的、多单元、离散作业型(Job Shop)制造车间为研究对象,围绕基于Holon的协商与自治调度关键技术(实体封装、控制体系结构、协商机制和核心算法)展开研究,提出了基于Holon概念模型的、集成强化学习机制的合同网协议和改进过滤定向搜索算法的自治与协商智能调度技术,以提供实用有效的求解方法、改善柔性制造车间适应内部动态环境和外部市场快速变化的能力。
具体地,本文的主要内容可概括为:
1.首先从基于Agent或Holon的自治与协商调度的关键技术(实体封装、控制体系结构、协商机制和核心算法)角度详细综述了近些年间基于Agent或Holon的自治与协商生产调度、核心技术的研究现状和存在问题,引出本文研究的出发点和意义;
2.针对可靠性、可扩展性和适应性等要求,提出了基于Holon的柔性制造车间控制体系结构,描述了组件Holon的结构模型、数据和功能关系、信息传递模型和通信语言基本规范,并从软件体系结构的观点出发,运用π演算对其进行了形式化描述和分析,为基于Holon的体系结构设计和分析奠定基础;
3.针对传统制造合同网协议缺乏优化和动态学习能力这一问题,将强化学习工具之一的Q-学习算法与制造合同网协议集成,形成CNP-QL(Contract Net Protocol-Q Leaning)协商机制,以提高Holon适应动态环境的实时协调调度能力。详细定义了CNP-QL机制的消息描述、策略决策过程、学习迭代过程和CNP-QL机制中Q-学习算法的各个要素(包括状态确定准则、状态空间划分、奖惩函数设计和搜索策略定义等)。最后,单元Holon和产品Holon之间通过CNP-QL机制协商,用于解决具有柔性工艺路径的多单元间的任务动态协调分配问题,并通过仿真实例与纯CNP机制进行了比较分析,验证了该机制的有效性;
4.调度算法是单元自治决策的引擎。在单元Holon任务协调分配后,提出了基于改进过滤定向搜索启发式算法HFBS(Heuristic based on Filtered-Beam-Search)的单元自治调度机制。构建了基于HFBS的单元自治调度决策模型。针对任务协调分配后形成的柔性Job Shop调度问题(Flexible Job-shop Scheduling Problem,FJSP),建立了面向多目标FJSP问题的数学规划模型并对模型进行分析讨论。为了有效解决FJSP问题,详细定义了HFBS四个搜索策略因素:(1)解空间的搜索树表述;(2)分枝策略;(3)定向搜索宽度和定向过滤宽度的确定;(4)评价函数的构造。最后,在分析策略因素对算法性能影响的基础上,就FJSP领域的标杆数据(Benchmark)和大量随机仿真数据,分别与现有的其它基于人工智能的启发式算法和调度规则进行了分析比较,说明了算法的有效性;
5.为了使单元自治调度实现持续优化,提高自治调度对不可预见或随机的内外扰动事件(如机床故障、新订单进入等)的实时适应能力,在系统化阐述动态重调度整体理论框架体系(包括重调度环境、策略、方法和技术)的基础上,提出柔性制造车间单元内的动态重调度决策过程,对基于过滤定向搜索的启发式核心算法进行局部/全局评价函数和分枝策略两方面的扩展以更好地集成作业交货期和优先权重,并从动态重调度对算法响应能力方面考虑,分析了算法的时间复杂性。最后,通过一系列实例仿真说明了基于过滤定向搜索算法能实现面向典型扰动的动态重调度并确保调度性能和反应效率。
6.最后,在JADE平台上,设计和开发了综合的原型仿真系统。以JADE Agent表述和实现了所定义的Holon之间交互和内部自治算法的控制技术,为将来基于自治与协商机制生产调度的工业化应用尝试可行的设计实现。
本文的研究成果在一定程度上推进和丰富了基于Holon的协商与自治调度方法关键技术的研究,有利于改善企业的科学生产管理和控制水平,对提高生产绩效和综合市场竞争能力具有一定的指导作用。同时,在改进和深化的基础上,本文研究思路和成果对于其它组合优化问题和复杂调度问题具有良好的求解潜力和应用前景。
本文在研究过程中得到了国家863项目(No.2003AA414120)、柔性制造系统技术国家重点实验室项目(No.51458060104JW0316)、2006教育部新世纪人才项目的大力支持和资助,在此表示衷心的感谢。
Scheduling problems in flexible manufacturing environment possess features of high complexity, uncertainty, multi-objects, coordination of multi-constraints and multi-resources, etc. Due to their important significances in theory and industrial practice, scheduling problems in flexible manufacturing environment have been extensively studied over the last five decades, and continue to attract the interests of researchers both in academia and industry.
To provide effective and efficient solutions to the scheduling problems in discrete job shop with multiple cells each having flexible process routings, this dissertation presents a Holon-based autonomic and coordinated intelligent scheduling technique, which is integrated with reinforcement-learning-based contract net protocol (CNP) and modified filtered-beam-search (FBS) algorithms, based on the recent researches in Agent or Holon-based decentrated production scheduling. The proposed intelligent scheduling technique is studied based on the key techniques of Agent or Holon-based autonomic and coordinated scheduling, i.e., encapsulation of entities, the control architectures, coordinated mechanism and core autonomic algorithms.
The main contributions of the dissertation are described as follows:
1. Firstly, the state-of-the-art of Agent or Holon-based autonomic and coordinated scheduling and the key techniques (i.e., encapsulation of entities, the control architectures, coordinated mechanism, core autonomic algorithms, as well as learning mechanism) are reviewed, and the existing problems in these fields are discussed, which leads to the driving force and significances of this research;
2. To fulfill the requirements of reliability, extensibility and adaptability, this dissertation proposes a Holon-based control architecture for flexible manufacturing shop floor using a bottom-up design approach. The inner structure of component Holon (mainly Cell Holon), the relationships of data and functions among Holons, the model of message transfer and the basic specification of communication language are described in detail. Then, from the aspect of software architecture, a formal description and analysis of the proposed architecture is conducted by First-order Polyadic pi-Calculus. This lays the foundation for the next Holon-based autonomic and coordinated scheduling.
3. Since it only defines the basic process of interactions among Agents or Holons, the basic contract net protocol for manufacturing scheduling is lack of the capability of optimization and dynamic learning. To overcome this issue and to improve its real-time adaptive capability for dynamic environment, a coordinated mechanism named after CNP-QL(Contract Net Protocol-Q Leaning)is proposed, which results from the integration of basic CNP for manufacturing scheduling and Q-leaning algorithm. To implement the CNP-QL mechanism, the main elements of the mechanism are defined and described, including the message description, the decision process, the interaction process for learning and key elements of Q-learning algorithm (e.g., the criterion of state determination, state space division, the reward function and the definition of search strategy, etc.). Then, to testify the effectiveness of the CNP-QL, the CNP-QL is used to solve the dynamic coordination problem of tasks among multiple cells with flexible process routings. And the effectiveness of the CNP-QL is demonstrated through simulations with comparison of the basic CNP.
4. Since the scheduling algorithm is the engine for the autonomic decision of a cell, this dissertation proposes a mechanism for autonomic scheduling of a cell, in which a heuristic based on Filtered-Beam-Search algorithm (HFBS) is as the core. To solve the Flexible Job-shop Scheduling problem after the task coordination among alternative cells, a mathematic model with multiple objectives is built and the analysis of the model complexity is discussed. Then, four key elements of the HFBS, namely, the solution space for the problem, the selection of beamwidth and filterwidth, the generation procedure of branches and selection of evaluation functions are illustrated in detail. Finally, based on the performance analysis of key elements of HFBS, the performance of HFBS are evaluated and compared with those of other AI-based heuristics and dispatching rules via benchmarks and simulation examples. The results demonstrate the effectiveness of HFBS.
5. To realize the continuous optimization of cell autonomic scheduling and to improve its real-time adaptive capability for unanticipated or stochastic internal and/or external disturbances (e.g., machine breakdown, new job arrival, etc.), a dynamic rescheduling decision process is proposed based on the description of theory of dynamic rescheduling (including rescheduling environment, strategy, approaches and techniques). Meanwhile, a FBS-based heuristic algorithm is proposed, which makes improvement of FBS algorithm in the generation procedure of branches and the local/global evaluation functions. Thus, it can easily consider and incorporate the due dates and priority weights of jobs and machine workload balance during the process of assignment jobs to machines. Then, the worst-case time complexity is analyzed. Finally, with respect to a due date-based objective, (weighted quadratic tardiness), computational experiments are conducted to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.
6.Finally, based on the JADE platform, a preliminary prototype is designed and developed, in which JADE Agent is used to represent Holons mentioned above. The main functions of negotiation between Holons and their autonomous scheduling decisions are shown in this prototype. This in turn will lay the foundation for the future practical implementation of Holon-based scheduling and rescheduling in shop floor.
The research makes some contributions in key techniques of Agent or Holon-based autonomic and coordinated scheduling approach. It can be used to improve the production management and control, and it can give some guidelines to enhance production performance and to improve the competitive capability in complex market. In addition, if modified and further studied in this direction, the thought and the results of this research is promising for solving other combinatorial optimization problems and complex scheduling problems, and it is also promising for practical potential implementation.
Acknowledgement This dissertation is supported by the National High Technology Research and Development Program of China under grant 2003AA414120, the National Key Lab. Program under grant 51458060104JW0316 and the Trans-Century Training Programme Foundation for the Talents by the State Education Commission 2006.
具体地,本文的主要内容可概括为:
1.首先从基于Agent或Holon的自治与协商调度的关键技术(实体封装、控制体系结构、协商机制和核心算法)角度详细综述了近些年间基于Agent或Holon的自治与协商生产调度、核心技术的研究现状和存在问题,引出本文研究的出发点和意义;
2.针对可靠性、可扩展性和适应性等要求,提出了基于Holon的柔性制造车间控制体系结构,描述了组件Holon的结构模型、数据和功能关系、信息传递模型和通信语言基本规范,并从软件体系结构的观点出发,运用π演算对其进行了形式化描述和分析,为基于Holon的体系结构设计和分析奠定基础;
3.针对传统制造合同网协议缺乏优化和动态学习能力这一问题,将强化学习工具之一的Q-学习算法与制造合同网协议集成,形成CNP-QL(Contract Net Protocol-Q Leaning)协商机制,以提高Holon适应动态环境的实时协调调度能力。详细定义了CNP-QL机制的消息描述、策略决策过程、学习迭代过程和CNP-QL机制中Q-学习算法的各个要素(包括状态确定准则、状态空间划分、奖惩函数设计和搜索策略定义等)。最后,单元Holon和产品Holon之间通过CNP-QL机制协商,用于解决具有柔性工艺路径的多单元间的任务动态协调分配问题,并通过仿真实例与纯CNP机制进行了比较分析,验证了该机制的有效性;
4.调度算法是单元自治决策的引擎。在单元Holon任务协调分配后,提出了基于改进过滤定向搜索启发式算法HFBS(Heuristic based on Filtered-Beam-Search)的单元自治调度机制。构建了基于HFBS的单元自治调度决策模型。针对任务协调分配后形成的柔性Job Shop调度问题(Flexible Job-shop Scheduling Problem,FJSP),建立了面向多目标FJSP问题的数学规划模型并对模型进行分析讨论。为了有效解决FJSP问题,详细定义了HFBS四个搜索策略因素:(1)解空间的搜索树表述;(2)分枝策略;(3)定向搜索宽度和定向过滤宽度的确定;(4)评价函数的构造。最后,在分析策略因素对算法性能影响的基础上,就FJSP领域的标杆数据(Benchmark)和大量随机仿真数据,分别与现有的其它基于人工智能的启发式算法和调度规则进行了分析比较,说明了算法的有效性;
5.为了使单元自治调度实现持续优化,提高自治调度对不可预见或随机的内外扰动事件(如机床故障、新订单进入等)的实时适应能力,在系统化阐述动态重调度整体理论框架体系(包括重调度环境、策略、方法和技术)的基础上,提出柔性制造车间单元内的动态重调度决策过程,对基于过滤定向搜索的启发式核心算法进行局部/全局评价函数和分枝策略两方面的扩展以更好地集成作业交货期和优先权重,并从动态重调度对算法响应能力方面考虑,分析了算法的时间复杂性。最后,通过一系列实例仿真说明了基于过滤定向搜索算法能实现面向典型扰动的动态重调度并确保调度性能和反应效率。
6.最后,在JADE平台上,设计和开发了综合的原型仿真系统。以JADE Agent表述和实现了所定义的Holon之间交互和内部自治算法的控制技术,为将来基于自治与协商机制生产调度的工业化应用尝试可行的设计实现。
本文的研究成果在一定程度上推进和丰富了基于Holon的协商与自治调度方法关键技术的研究,有利于改善企业的科学生产管理和控制水平,对提高生产绩效和综合市场竞争能力具有一定的指导作用。同时,在改进和深化的基础上,本文研究思路和成果对于其它组合优化问题和复杂调度问题具有良好的求解潜力和应用前景。
本文在研究过程中得到了国家863项目(No.2003AA414120)、柔性制造系统技术国家重点实验室项目(No.51458060104JW0316)、2006教育部新世纪人才项目的大力支持和资助,在此表示衷心的感谢。
Scheduling problems in flexible manufacturing environment possess features of high complexity, uncertainty, multi-objects, coordination of multi-constraints and multi-resources, etc. Due to their important significances in theory and industrial practice, scheduling problems in flexible manufacturing environment have been extensively studied over the last five decades, and continue to attract the interests of researchers both in academia and industry.
To provide effective and efficient solutions to the scheduling problems in discrete job shop with multiple cells each having flexible process routings, this dissertation presents a Holon-based autonomic and coordinated intelligent scheduling technique, which is integrated with reinforcement-learning-based contract net protocol (CNP) and modified filtered-beam-search (FBS) algorithms, based on the recent researches in Agent or Holon-based decentrated production scheduling. The proposed intelligent scheduling technique is studied based on the key techniques of Agent or Holon-based autonomic and coordinated scheduling, i.e., encapsulation of entities, the control architectures, coordinated mechanism and core autonomic algorithms.
The main contributions of the dissertation are described as follows:
1. Firstly, the state-of-the-art of Agent or Holon-based autonomic and coordinated scheduling and the key techniques (i.e., encapsulation of entities, the control architectures, coordinated mechanism, core autonomic algorithms, as well as learning mechanism) are reviewed, and the existing problems in these fields are discussed, which leads to the driving force and significances of this research;
2. To fulfill the requirements of reliability, extensibility and adaptability, this dissertation proposes a Holon-based control architecture for flexible manufacturing shop floor using a bottom-up design approach. The inner structure of component Holon (mainly Cell Holon), the relationships of data and functions among Holons, the model of message transfer and the basic specification of communication language are described in detail. Then, from the aspect of software architecture, a formal description and analysis of the proposed architecture is conducted by First-order Polyadic pi-Calculus. This lays the foundation for the next Holon-based autonomic and coordinated scheduling.
3. Since it only defines the basic process of interactions among Agents or Holons, the basic contract net protocol for manufacturing scheduling is lack of the capability of optimization and dynamic learning. To overcome this issue and to improve its real-time adaptive capability for dynamic environment, a coordinated mechanism named after CNP-QL(Contract Net Protocol-Q Leaning)is proposed, which results from the integration of basic CNP for manufacturing scheduling and Q-leaning algorithm. To implement the CNP-QL mechanism, the main elements of the mechanism are defined and described, including the message description, the decision process, the interaction process for learning and key elements of Q-learning algorithm (e.g., the criterion of state determination, state space division, the reward function and the definition of search strategy, etc.). Then, to testify the effectiveness of the CNP-QL, the CNP-QL is used to solve the dynamic coordination problem of tasks among multiple cells with flexible process routings. And the effectiveness of the CNP-QL is demonstrated through simulations with comparison of the basic CNP.
4. Since the scheduling algorithm is the engine for the autonomic decision of a cell, this dissertation proposes a mechanism for autonomic scheduling of a cell, in which a heuristic based on Filtered-Beam-Search algorithm (HFBS) is as the core. To solve the Flexible Job-shop Scheduling problem after the task coordination among alternative cells, a mathematic model with multiple objectives is built and the analysis of the model complexity is discussed. Then, four key elements of the HFBS, namely, the solution space for the problem, the selection of beamwidth and filterwidth, the generation procedure of branches and selection of evaluation functions are illustrated in detail. Finally, based on the performance analysis of key elements of HFBS, the performance of HFBS are evaluated and compared with those of other AI-based heuristics and dispatching rules via benchmarks and simulation examples. The results demonstrate the effectiveness of HFBS.
5. To realize the continuous optimization of cell autonomic scheduling and to improve its real-time adaptive capability for unanticipated or stochastic internal and/or external disturbances (e.g., machine breakdown, new job arrival, etc.), a dynamic rescheduling decision process is proposed based on the description of theory of dynamic rescheduling (including rescheduling environment, strategy, approaches and techniques). Meanwhile, a FBS-based heuristic algorithm is proposed, which makes improvement of FBS algorithm in the generation procedure of branches and the local/global evaluation functions. Thus, it can easily consider and incorporate the due dates and priority weights of jobs and machine workload balance during the process of assignment jobs to machines. Then, the worst-case time complexity is analyzed. Finally, with respect to a due date-based objective, (weighted quadratic tardiness), computational experiments are conducted to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.
6.Finally, based on the JADE platform, a preliminary prototype is designed and developed, in which JADE Agent is used to represent Holons mentioned above. The main functions of negotiation between Holons and their autonomous scheduling decisions are shown in this prototype. This in turn will lay the foundation for the future practical implementation of Holon-based scheduling and rescheduling in shop floor.
The research makes some contributions in key techniques of Agent or Holon-based autonomic and coordinated scheduling approach. It can be used to improve the production management and control, and it can give some guidelines to enhance production performance and to improve the competitive capability in complex market. In addition, if modified and further studied in this direction, the thought and the results of this research is promising for solving other combinatorial optimization problems and complex scheduling problems, and it is also promising for practical potential implementation.
Acknowledgement This dissertation is supported by the National High Technology Research and Development Program of China under grant 2003AA414120, the National Key Lab. Program under grant 51458060104JW0316 and the Trans-Century Training Programme Foundation for the Talents by the State Education Commission 2006.
引文
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