基于MAS的车间调度控制系统的研究

讨论了分布式控制结构和传统的递阶式控制结构的特点，面向任务建立了基于MAs的调度控制系统模型并建立了agent间的协作模型以及通信模型。整个系统运用动态重组的思想，采用分级分布式控制和并行处理相结合的组织结构和运行模式，将整个车间的调度问题分解为一系列子调度问题，满足了现代制造系统对动态性、柔性和敏捷性的要求。     

基于MAS的复杂系统分布式求解策略与推理研究

针对复杂的连续生产线系统监测与故障诊断的动态、分布、实时和不确定特性，将多Agent及相关智能技术引入复杂故障诊断领域，提出了一种嵌套式基于消息传递的多Agent组织模型，分析了基于MAS的分布式智能故障诊断方法和过程；研究了模型系统的动态适应性和稳定性；设计了实时诊断Agent工作状态的表达机制；讨论了Agent间的协调协作机制及融合方法；给出了多Agent诊断系统诊断决策的集成描述结构；提出了任务分解与分配调度算法，以充分发挥Agent的社会性和基于场景的特点，使得在复杂系统的问题求解中能提供更为可靠的诊断结果，而且还能节约资源提高诊断效率．将其应用于某安全监控系统中，取得了与专家相似的诊断结果，克服了以往监控诊断系统存在的弊端，提高了企业的安全运行效率．与传统诊断方法相比，体现了Agent技术在复杂分布式问题求解领域的特有优势和良好前景．     

基于混合结构的机器人Agent控制结构的研究

该文面向分布Agent多移动机器人系统,提出了一种适合于多移动机器人的机器人Agent分层式体系结构,包括状态监测层、决策规划层、协调控制层和行为控制层,其中状态监测层主要实现整个系统对外部环境的状态监测。决策规划层设定系统的全局目标和单个机器人的局部目标,合理快速地完成任务的分解和分配,实现机器人之间任务级之间的协作。协调控制层完成机器人之间的运动协调。行为控制器主要采用基于行为的方法实现具体的运动控制。该结构应用于RoboCup环境下的分布多机器人系统中,满足复杂的、动态的应用环境和系统要求。     

可再生能源分布式发电能量管理的复杂性研究

本文主要研究可再生能源分布式发电系统能量的优化与协调问题.基于系统能量管理中优化与协调控制问题的复杂性,提出采用基于Agent的建模与仿真方法进行研究.针对系统要求,以分散式发电系统的实体节点的映射封装Agent,提出了智能混合控制Agent的概念,建立了3层混合Agent的内部结构模型;并建立了以个体层、组织层和社会层构成的3层动态层级MAS体系结构,进一步辅以主导Agent和移动Agent辅助系统优化与决策,最终建立了一种新的面向再生能源的分散式发电系统能量管理的MAS宏观模型.最后应用有色Petri网对系统的优化与协调过程进行了动态模拟,验证了所提出的结构的正确性、合理性和有效性.     

协作企业制造过程多Agent调度建模技术

构建了面向订单的基于WWW协作企业制造过程模型。通过车间调度的多Agent协作流程,建立车间调度多Agent实体的UML描述方式。采用合同网协议,把交货时间、生产成本和设备利用率等作为多Agent投标时的性能指标,形成分布式产品调度动态处理模型。最后通过一个实例来验证多Agent调度建模技术的可行性。     

多移动机器人系统个体控制体系结构

本文面向多移动机器人系统,提出了一种适合于移动机器人个体的分层式体系结构, 包括系统监控层、协作规划层和行为控制层三个层次．其中系统监控层主要实现人对系统的 实时监控功能;协作规划层在与其它机器人相应层的交互过程中建立系统的分层式组织形式 ,合理快速地完成任务的分解和分配,实现了机器人之间的任务级协作;行为控制层主要采 用基于行为的方法实现具体的运动控制．该结构满足了移动机器人渐趋复杂的应用环境和日 益增大的系统规模的要求．     

基于Agent的混合流水车间动态调度系统

针对敏捷制造调度环境的不确定性、动态性以及混合流水车间（HFS）调度问题的特点,设计了一种基于多Agent的混合流水车间动态调度系统,系统由管理Agent、策略Agent、工件Agent和机器Agent构成。首先提出一种针对混合流水车间环境的插值排序（HIS）算法并集成于策略Agent中,该算法适用于静态调度和多种动态事件下的动态调度。然后,设计了各类Agent间的协调机制,在生产过程中所有Agent根据各自的行为逻辑独立工作并互相协调。在发生动态事件时,策略Agent调用HIS算法根据当前车间状态产生工件序列,随后各Agent根据生成的序列继续进行协调直到完成生产。最后进行了发生机器故障、订单插入情况下的重调度以及在线调度等动态调度的实例仿真,结果表明对于这些问题,HIS算法的求解效果均优于调度规则,特别是在故障重调度中,HIS算法重调度前后的Makespan一致度达97.6%,说明系统能够灵活和有效地处理混合流水车间动态调度问题。     

虚拟制造环境下基于多智能体的敏捷调度策略的研究

该文针对虚拟制造环境下生产作业调度,把多智能体系统的分布式结构和层次式结构有机地结合起来,提出基于多Agent的虚拟环境下车间生产敏捷调度的混合层次结构,并着重介绍了Agent之间的基于招投标机制的谈判协议     

基于Agent的分布式动态作业车间调度

Agent技术是分布式工业系统建模的一种重要方法．本文对Agent及多Agent技术进 行了简要总结,综述了Agent技术在制造作业车间调度中的应用研究概况,提出了一种基于 合同网协议投标机制的多Agent分布式动态作业车间调度方案．     

基于MAS的生产车间动态调度系统的研究

简要介绍了车间调度的基本问题,发展现状以及多智能体技术.在分析车间生产调度特点的基础上,提出了基于MAS的动态车间生产调度模型.该模型把车间生产调度系统分为调度代理、任务代理和资源代理等.代理之间采用了基于改进的合同网的关系网模型,并引入了基于混合遗传算法的调度模块,为解决车间加工动态调度问题提供了一种新的方法.仿真实验结果表明,该系统更好满足了车间调度的动态化、高效化、智能化、实用化的要求.     

Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition

Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on two case studies.     

A multi-agent based agile manufacturing planning and control system

In today’s manufacturing enterprise, the performance of customer service level (e.g., short ordering-to-delivery time, low price) is highly dependent on the effectiveness of its manufacturing planning and control system (MPCS). However, most of the current MPCS, employed the hierarchical planning approach, may have some drawbacks, such as structural rigidity, difficulty of designing a control system, and lack of flexibility. Currently, RFID (Radio Frequency Identification) technology has been applied to enhance the visibility, accountability, track ability and traceability of manufacturing system whenever the accurate and detailed manufacturing information (e.g., raw material, WIP, products in factory and products in the down streams) of products will be followed in real-time basis by RFID technique. In addition, a multi-agent approach may be applied in a distributed and autonomous system which allows negotiation-based decision making. Therefore, the objective of this research is to study the application of RFID technique and multi-agent system (MAS) in developing an agent-based agile manufacturing planning and control system (AMPCS) to respond to the dynamically changing manufacturing activities and exceptions.In AMPCS, RFID-based manufacturing control (R-MC) module plays the role of controlling the manufacturing system in which production items and manufacturing resources attached with RFID tag may actively feedback production status to and receive production and operations schedule from advanced manufacturing planning (AMP) module. In addition, a bidding process and algorithm is developed to generate operations schedule by using the characteristics of MAS. Performance analysis (PA) module is responsible not only for evaluating the scheduling results but also for evaluating the performance of production execution. The development of an AMPCS for an automated manufacturing cell demonstrates that the integration of RFID technique and MAS in developing an agile manufacturing planning and control system can really possess the characteristics of visibility, accountability, track ability, responsiveness, and flexibility in a distributed and dynamic manufacturing system.     

An integrated agent-based approach for responsive control of manufacturing resources

Due to the dynamic fluctuation of customer demands in the global market, manufacturing enterprises are facing difficulties in rapidly responding to market changes. The aim of this research is to develop a system to integrate dynamic process planning and dynamic production scheduling for the purpose of increasing the responsiveness of adaptive manufacturing systems in accommodating dynamic market changes (rapidly changing demand patterns or product varieties). The concept of Multi-Agent Systems (MAS) has been adopted in this study. All of the tasks related to process planning, optimization and scheduling in this system are carried out by autonomous agents that are capable of interacting and negotiating with each other to bid for jobs and make decisions. On the one hand, this system optimizes the utilization of manufacturing resources and on the other hand, it also provides a platform where the reconfiguration of manufacturing systems can be assessed. This system has been implemented on a Java platform and a case study is provided to elaborate on this system and evaluate its implementation.     

基于多Agent柔性制造单元的生产调度研究

针对基于制造单元的作业车间的生产调度问题进行了研究,结合多代理的智能性、灵活性和遗传算法的智能优化能力,建立基于多智能体的柔性制造单元的作业车间的调度系统模型.然后,提出了集成多智能体和遗传算法的动态调度策略和调度协商机制;最后,应用此方法完成了常规调度和异常调度的仿真算例.结果表明所开发系统可以解决基于加工单元的制造...     

Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model

Due to the dynamic nature, complexity, and interactivity of production scheduling in an actual business environment, suitable combined and hybrid methods are necessary. This paper takes prefabricated concrete components as an example and develops the dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) to optimize and simulate the production scheduling. First, a multiobjective genetic algorithm is integrated into the MAS for preliminary optimization and a series of near‐optimal solutions are obtained. Subsequently, considering the resource constraints and uncertainties, the MAS is used to simulate complex real‐world production environments. Considering the different types of uncertainty factors, the paper proposes the corresponding dynamic scheduling method and uses MAS to generate the optimal production schedule. Finally, a practical prefabricated construction case is used to validate the proposed model. The results show that the model can effectively address the occurrence of uncertain events and can provide dynamic decision support for production scheduling.     

一类基于多Agent和分布式规则的敏捷生产调度

Agent范例为解决制造系统的敏捷生产调度问题提供了一条新途径,如何构建敏捷生产调度多Agent系统结构和Agent间的协调与生产调度机制,成为一个亟待解决的课题.本文阐述了一类基于多Agent和分布式规则构建敏捷生产调度的方法.首先通过基于功能分解的方法,给出了管理、资源和工件等三类Agent基本组件组成的分布式多Agent调度系统结构、Agent组件基本结构及定义.其次,利用基于分布式规则的方法,建立了Agent间的协调策略和调度机制,实现了敏捷生产调度.最后给出了应用此方法的调度仿真实验结果.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

Hyper-distributed hyper-parallel self-organizing dynamic scheduling based on solitary wave

This paper presents a new soliton approach to hyper-distributed hyper-parallel self-organizing dynamic scheduling for task allocations among rational autonomous agents in a multi-agent system(MAS).This approach can overcome many drawbacks of other mechanisms currently used for coalition formation and cooperation in MAS.The thorny problems,such as overabundant bid .Social behaviors,colony intelligence,variable neighbors,and interdependency,can easily be treated by using the proposed approach,whereas they are very difficult for other conventional approaches,.The simulation on a distributed transport scheduling system shows the soliton approach featured by hyper-parallelism,effectiveness,openness,dynamic alignment and adaption.     

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.