A multi-agent based approach for change management in manufacturing enterprises

Change management becomes an unavoidable necessity for manufacturing enterprises. Since change in business processes carries significant impact on the performance of manufacturing companies, a change management model is definitely required to remain competitive. Moreover, utilizing agent based systems will provide computational provision and integrity to manage and measure the capabilities to follow the change in a progressive approach by employing the cooperation and collaboration properties of various agents helping for retrieval of the required information in a rapid way. Therefore, in this paper, a multi-agent based change management model is proposed to handle the changes in manufacturing enterprises. The model is validated through a case study done to measure the performance of change management capabilities in a manufacturing company. A sensitivity analysis on the results of this case study is also conducted to reveal the system reactivity to various parameters.     

A multi-agent system for meteorological radar data management and decision support

The continuous processing and evaluation of meteorological radar data require significant efforts by scientists, both for data processing, storage, and maintenance, and for data interpretation and visualization. To assist meteorologists and to automate a large part of these tasks, we have designed and developed Abacus, a multi-agent system for managing radar data and providing decision support. Abacus' agents undertake data management and visualization tasks, while they are also responsible for extracting statistical indicators and assessing current weather conditions. Abacus agent system identifies potentially hazardous incidents, disseminates preprocessed information over the web, and enables warning services provided via email notifications. In this paper, Abacus' agent architecture is detailed and agent communication for information diffusion is presented. Focus is also given on the customizable logical rule-bases for agent reasoning required in decision support. The platform has been tested with real-world data from the Meteorological Service of Cyprus.     

基于多Agent的政府知识管理系统模型研究

针对现有政府知识管理系统的局限性和政府知识管理的复杂性,采用面向多Agent的系统设计方法,设计了一个基于多Agent的政府知识管理系统模型,给出了系统模型实现的一些关键技术.该模型由多个Agent来协同实现政府知识的异构整合、知识推送、知识利用等服务,以满足政务知识管理的异构知识集成、智能化决策、个性化服务等实际需求.     

一种新的基于数据挖掘技术的异常入侵检测系统研究

为了解决异常入侵检测系统中出现的噪音数据信息干扰、不完整信息挖掘和进攻模式不断变化等问题,提出了一种新的基于数据挖掘技术的异常入侵检测系统模型。该模型通过数据挖掘技术、相似度检测、滑动窗口和动态更新规则库的方法,有效地解决了数据纯净难度问题,提高了检测效率,增加了信息检测的预警率,实现了对检测系统的实时更新。     

A multi-agent system for web-based risk management in small and medium business

Business Intelligence has gained relevance during the last years to improve business decision making. However, there is still a growing need of developing innovative tools that can help small to medium sized enterprises to predict risky situations and manage inefficient activities. This article present a multi-agent system especially created to detect risky situations and provide recommendations to the internal auditors of SMEs. The core of the multi-agent system is a type of agent with advanced capacities for reasoning to make predictions based on previous experiences. This agent type is used to implement a evaluator agent specialized in detect risky situations and an advisor agent aimed at providing decision support facilities. Both agents incorporate innovative techniques in the stages of the CBR system. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.     

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 agent-oriented and knowledge-based system for strategic e-procurement

Abstract: This paper presents an agent‐oriented and knowledge‐based system (AOKBS) for strategic e‐procurement. The AOKBS has been designed to capture and leverage the knowledge of an enterprise to generate dynamic business rules by which an effective procurement strategy can be generated based on enterprise needs and the analysis of relevant market conditions. The performance and practices of the suppliers can also be evaluated and captured continuously, respectively, by the AOKBS. Artificial intelligence technologies such as case‐based reasoning and agent technology are incorporated for decision analysis. A prototype e‐procurement system has been built and implemented successfully with a multinational manufacturer.     

A new multi-agent system framework for tacit knowledge management in manufacturing supply chains

Participating members in a manufacturing supply chain (MSC) usually make use of individual knowledge for making independent decisions. Recent research, however, indicates that there is a need to handle such distributed knowledge in an integrated manner, especially under uncertain and fast changing environments. A multiagent system (MAS), a branch of distributed artificial intelligence, is a contemporary modelling technique for a distributed system like MSCs in the manufacturing domain. However recent researches indicate that MAS approaches have not adequately addressed the role of sharing tacit knowledge (TK) on MSC performance. This paper, therefore, aims to propose a framework that utilizes MAS techniques with a corresponding TK sharing mechanism dedicated to MSCs. We performed some experiments to simulate the proposed approach. The results showed significant improvements when comparing the proposed approach with another conventional MAS model. The results establish a starting point for researchers interested in enhancing MSC performance using TK management approach, and for managers of MSC to focus on the essentials of sharing TK.     

基于多Agent的维修任务调度系统的研究

本文根据维修作业任务调度的复杂性,以及当前存在的问题,提出了基于Multi-Agent技术的维修作业任务调度系统,建立了基于管理Agent、任务Agent和资源Agent的系统模型,引入了规则调度机制和消息报告机制,并且讨论了优先级的计算算法。     

A Q-learning-based multi-agent system for data classification

In this paper, a multi-agent classifier system with Q-learning is proposed for tackling data classification problems. A trust measurement using a combination of Q-learning and Bayesian formalism is formulated. Specifically, a number of learning agents comprising hybrid neural networks with Q-learning, which we have formulated in our previous work, are devised to form the proposed Q-learning Multi-Agent Classifier System (QMACS). The time complexity of QMACS is analyzed using the big O-notation method. In addition, a number of benchmark problems are employed to evaluate the effectiveness of QMACS, which include small and large data sets with and without noise. To analyze the QMACS performance statistically, the bootstrap method with 95% confidence interval is used. The results from QMACS are compared with those from its constituents and other models reported in the literature. The outcome indicates the effectiveness of QMACS in combining the predictions from its learning agents to improve the overall classification performance.     

A multi-agent based user context Bayesian neural network analysis system

The increasing user mobility demands placed upon IT services necessitates an environment that enables users to access optimal services at any time and in any place. This study presents research conducted to develop a system that is capable of analyzing user IT service patterns and tendencies and provides the necessary service resources by sharing each user’s context information. First, each user’s context information is gathered to provide the multi-agent software training data necessary to describe user operations in a hybrid peer-to-peer (P2P) structured communication environment. Next, the data collected about each user’s mobile device is analyzed through a Bayesian based neural network system to identify the user’s tendency and extract essential service information. This information provides a communication configuration allowing the user access to the best communication service between the user’s mobile device and the local server at any time and in any place, thereby enhancing the ubiquitous computing environment.     

一种适合EAI的多Agent系统模型

企业应用集成是当前研究的热点，应用集成是能够满足企业统一信息管理、统一资源调配和优化业务流程的重要手段。目前应用集成尚无公认的标准，在实施集成中主要考虑标准接口问题，而往往忽略本身体系结构的合理性。要满足企业为适应市场竞争而灵活变动的管理模式，应用集成必须具备较高的可扩展性和可灵活调整性。在分析企业应用集成内涵的基础上，提出了适合于企业应用集成特点的多Agent系统模型。     

多代理分层的智能配电网自愈控制研究

介绍了配电网自愈的基本概念,对传统的框架体系进行了改进,并提出一种基于智能多代理分层的配电网自愈控制技术。根据配电网的功能进行了分层,将智能多代理技术与电网的运行状态相结合,根据不同的运行状态执行不同的控制策略,并充分利用多代理技术的自治性和协同性,为智能配电网自愈控制提供理论和方法上的依据。     

基于Multi-Agent的协作学习系统

针对当前的协作学习系统很少考虑到学习者知识水平、认知特性、兴趣等个性化属性，提出了一个基于学习者知识水平的分层多Agent学习系统模型，该模型结合智能代理技术，通过对学习者知识水平的界定实现系统的分层结构，较好地解决了现有学习系统中普遍存在而又尚未解决的无序和混乱、群体互动效果、个性化以及系统通讯开销等问题。     

An exception handling framework for case management

Software and Systems Modeling - In order to achieve their business goals, organizations heavily rely on the operational excellence of their business processes. In traditional scenarios, business...     

Route planning model of multi-agent system for a supply chain management

Sometimes in travel planning, finding the best route to the road transportation network by considering the environmental conditions that are affecting the actual time travel of the travellers are vital especially in handling the logistic operations in supply chain management (SCM). Furthermore, the policy strategy is needed in order to influence the managers or drivers to find the optimum and the most effective route for a trip plan in supporting the logistic operations of SCM. In this paper we analyze the effectiveness of the coordination model of the environmental conditions that are affecting for the travelling time based on multi-agent system for a road transportation network for supply chain management. A number of experimental cases have been used to evaluate the proposed approach transportation network problems in some Malaysian cities. Finally, experimental results affirmed that the proposed approach is practical and efficient.     

A multi-agent legal recommender system

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.     

A self-adaptation scheme for workflow management in multi-agent systems

Business processes, operational environment, variability of resources and user needs may change from time to time. An effective workflow management software system must be able to accommodate these changes. The ability to dynamically adapt to changes is a key success factor for workflow management systems. Holonic multi-agent systems (HMS) provide a flexible and reconfigurable architecture to accommodate changes based on dynamic organization and collaboration of autonomous agents. Although HMS provides a potential architecture to accommodate changes, the dynamic organization formed in HMS poses a challenge in the development of a new software development methodology to dynamically compose the services and adapt to changes as needed. This motivates us to study and propose a methodology to design self-adaptive software systems based on the HMS architecture. In this paper, we formulate a workflow adaptation problem (WAP) and propose an interaction mechanism based on contract net protocol (CNP) to find a solution to WAP to compose the services based on HMS. The interaction mechanism relies on a service publication and discovery scheme to find a set of task agents and a set of actor agents to compose the required services in HMS. We propose a viable self-adaptation scheme to reconfigure the agents and the composed services based on cooperation of agents in HMS to accommodate the changes in workflow and capabilities of actors. We propose architecture for our design methodology and present an application scenario to illustrate our idea.     

A multi-agent intelligent system for efficient ERP maintenance

The Enterprise Resource Planning (ERP) system is an enterprise-wide integrated software package designed to uphold the highest quality standards of business process. However, for the time being, when the business condition has been changed, the system may not guarantee that the process embedded in ERP is still best. Moreover, since the ERP system is very complex, maintaining the system by trial and error is very costly. Hence, this paper aims to construct a support system that adjusts ERP system to environmental changes. To do so, we adopt multi-agent intelligent technology that enables autonomous cooperation with one another to monitor ERP databases and to find any exceptional changes and then analyze how the changes will affect ERP performance. Moreover, Petri net is applied to manage the complexity and dynamics of agents’ behavior. To show the feasibility of the idea, a prototype agent system, ERP/PN, is proposed and an experiment is conducted.     

多智能体的机器人逆运动学算法研究

提出了一种基于多智能体的逆运动学算法,算法采用多个关节智能体协同进行串行和并行运算求解,适用于绝大多数关节机器人,不受关节形状和关节数目的限制。