An ontology based multi-agent system to support HABIO outsourcing framework

In recent years the widespread application of outsourcing to help improve finance and maintain competitiveness is growing rapidly, and has been accompanied by a growth of academic research in this field. Most of the outsourcing literature has focused primarily on the social and cultural impacts, the organisational and management perspectives of outsourcing and very few tools have been developed to assist managers in making outsourcing decisions. Incorrect decision-making in outsourcing can lead to devastating consequences for their business. To guide decision makers in managing the complexity of outsourcing a holistic approach to outsourcing has been developed by taking business, information and organisational perspectives (HABIO). The purpose of this paper is to demonstrate how HABIO can be applied to help managers evaluate the impact of their decision-making by providing them with a set of ‘what-if’ scenarios, which are enacted by intelligent agents. The case example, discussed in this paper, demonstrates that HABIO can be applied effectively using a multi-agent system to analyse the outsourcing decision for a call centre sector in the retail industry. A prototype of the multi-agent system is presented where intelligent agents are endowed with specific domain ontologies, appropriate to solve a specific problem, and with a reasoning mechanism to achieve specific goals and to collaborate together to produce a set of “what-if scenarios” to the outsourcing managers.     

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.     

基于合同网协议的多智能体虚拟企业协商研究

协商是虚拟企业成员之间协作的实现方式。本文通过对多智能体的协商框架、协商模型和协商过程等进行深入的分析,在合同网协议的基础上构建多智能体协商体系框架,并结合贝叶斯决策方法来建立自学习协商模型。根据协商中的更新信息实时更新智能体信念,实现多智能体协商过程的自学习功能。从而有利于提高协商效率,改善网络通信,保证虚拟企业的敏捷性和协商双方的利益。     

Development of an agent-based Virtual CIM architecture for small to medium manufacturers

Today's global competitions are forcing enterprises to rely on integrated manufacturing systems to satisfy constantly changing market requirements. Whereas Small to Medium Enterprises (SMEs) often feel difficult to compete with large organisations due to having insufficient resources. By integrating resources of many partners, the SMEs can form global integrated manufacturing systems in the form of Virtual CIM (VCIM) and thus achieve a competitive edge. VCIM is a concept towards integrating globally distributed manufacturing resources across enterprise boundaries. In order to implement VCIM, this research project develops an agent-based architecture to support the integration and scheduling of distributed manufacturing resources. This architecture accommodates all agents for VCIM with a three-layered structure and permits new agents to be connected with the existing structure through the Internet when and where necessary. In this architecture, many Facilitator agents coordinate activities of manufacturing resources in a parallel manner. Meanwhile, the Facilitator agents achieve proposal messages through agent negotiation based on distributed resources’ real time information and use a backward network algorithm for shortest-path to perform optimisation for resource allocation. With these approaches, an optimised production schedule has the lowest cost as the primary criteria and the shortest production time as the secondary criteria while satisfying customer required due date/time and delivering destination.     

Reactive multi-agent system for assembly cell control

This paper presents a multi-agent system for the control of manufacturing systems. The multi-agent system is designed to provide manufacturing control with three important characteristics: high robustness, quick response and good expandability. A prototype multi-agent-based control system has been developed for a flexible assembly cell as an example. The prototype multi-agent system contains several reactive agents. Each agent acts according to its built-in behaviours, and the behaviours are able to respond to stimuli from the manufacturing environment. A system architecture to implement the multi-agent-based control system is proposed and the coordination model of the reactive agents is developed. The generic agent structure is established for individual reactive agents, and the behaviours and the subsumption architecture of each agent are designed.     

A hybrid multi-agent system architecture for enterprise integration using computer networks

The manufacturing industries are now experiencing fierce pressure of competition from every corner on this planet. In addition, the advancement in computer networks and information technologies has been gradually reshaping the manufacturing companies by shifting from the industrial age to the information era. Due to these elevated competitiveness and advanced computer technology, a number of new manufacturing and management strategies (e.g., CE or CIM) have emerged for the innovation of manufacturing enterprises. Although they have different definitions and scopes, there are several common issues: inter-enterprise functions integration; inter-enterprise resources integration; and collaboration. This paper proposes a new multi-agent system (MAS) architecture to support the inter-enterprise functions/resources integration and collaboration over the networked environment, including the hybrid agent architecture and hybrid network architecture. In contrast to the existing agent architectures, the proposed agent architecture enables agents to exhibit the hybrid (continuous and discrete) behavior and interactions. In addition, our network architecture is more suitable for building the large-scale distributed manufacturing systems that are prone to dynamic random changes of their environment. Based on the proposed MAS architecture, a collaborative product development environment is implemented as a starting point, and a multidisciplinary team-oriented design problem is illustrated to provide the vision of the proposed MAS architecture.     

Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system

Personalized production has emerged as a result of the increasing customer demand for more personalized products. Personalized production systems carry a greater amount of uncertainty and variability when compared with traditional manufacturing systems. In this paper, we present a smart manufacturing system using a multi-agent system and reinforcement learning, which is characterized by machines with intelligent agents to enable a system to have autonomy of decision making, sociability to interact with other systems, and intelligence to learn dynamically changing environments. In the proposed system, machines with intelligent agents evaluate the priorities of jobs and distribute them through negotiation. In addition, we propose methods for machines with intelligent agents to learn to make better decisions. The performance of the proposed system and the dispatching rule is demonstrated by comparing the results of the scheduling problem with early completion, productivity, and delay. The obtained results show that the manufacturing system with distributed artificial intelligence is competitive in a dynamic environment.     

Integrated model of operations effectiveness of small to medium-sized manufacturing enterprises

The Small to Medium-sized Manufacturing Enterprise (SME) has consistently demonstrated the ability to innovate and bring to the market new technologies and advancement in manufacturing. SMEs in industrialized nations function as the supplier base for domestic manufacturing. However, as more businesses are going global, SMEs face a new challenge in competing in the competitive global market. In this research, we aim to answer the following question: by taking advantage of the challenge as new opportunities, how SMEs can reposition themselves to be more competitive in the global market. We analyze the data collected from over 3000 SMEs using an integrated productivity-oriented model. The results present us with information about SMEs’ operation and performance. We are able to link both qualitative and quantitative factors of multiple manufacturing input/output to further assess the performance of SMEs, making a set of efficiency benchmarks available. This information can be used to help SMEs better understand the problems and opportunities confronting their operations. So their business transformations can be cost-effectively conducted for more competitive edges.     

Proposing a graph ranking method for manufacturing system selection in high-tech industries

All manufacturing centers are looking for the solutions to reduce costs and increase their competitive advantages. One of the practical solutions for cost reduction is to select a suitable manufacturing system in order to optimize usage of limited resources. In high-tech industries, the manufacturing system selection is extremely difficult because of the complex features and structures of their products. Generally, selecting the best manufacturing system of high-tech products is a multiple-criteria decision-making (MCDM) problem. Graph ranking method is one of the most used techniques among MCDM methods, which is originated from combinatorial mathematics. Simple computational procedure, ability to consider relationships between criteria, etc., are some perfect characteristics of this method for modeling and solving decision-making problems with complexity. Therefore, this study attempted to determine the most suitable manufacturing system in high-tech industries using graph ranking method. Moreover, because of vagueness and imprecision in human judgments, fuzzy set theory is utilized in the evaluation procedure. The suggested approach was used to select the most appropriate system for LCD manufacturing at Sanam Electronic Company. Finally, Obtained results indicated the efficiency of the proposed approach and selection of a cloud-based manufacturing system as the most suitable manufacturing system in high-tech industries.

     

Design of adaptive function blocks for dynamic assembly planning and control

In today’s competitive market, the life cycle of products is shrinking while product variety is growing. The product mix in small batches contributes to manufacturing uncertainty. Meanwhile, ever-growing business globalization and outsourcing also influence manufacturing. In such a dynamic environment, adaptability is of great importance. It is essential to develop a system that enables not only adaptive decision making, but also effective decision execution. Targeting manufacturing uncertainty, this paper reports a new framework and methodology for adaptive assembly planning using function blocks, which can be used directly for assembly control.     

Agent-based simulation of competitive and collaborative mechanisms for mobile service chains

A new paradigm for a mobile service chain’s competitive and collaborative mechanism is proposed in this study. The main idea of the proposed approach is based on a multi-agent system with optimal profit of the pull, push, and collaborative models among the portal access service provider (PASP), the product service provider (PSP), and the mobile service provider (MSP). To address the running mechanism for the multi-agent system, an integrated system framework is proposed based on the agent evolution algorithm (AEA), which could resolve all these modes. To examine the feasibility of the framework, a prototype system based on Java-Repast is implemented. The simulation experiments show that this system can help decision makers take the appropriate strategies with higher profits. By analyzing the expectations and variances (or risks) of each player’s profit, the interaction between and among entities in the chain is well understood. It is found that in the situation where a collaborative mechanism is applied, the performance of players is better as compared to the other two situations where a competitive mechanism is implemented. If some constraints are applied, the risk will be kept at a low level.     

A decision support system for cooperative transportation planning: Design,implementation, and performance assessment

In this paper, we describe a decision support system for cooperative transportation planning in the German food industry where several manufacturing companies share their fleets to reduce transportation costs. Besides using vehicles of their fleets, there are different outsourcing options offered by logistics service providers, but these are much more expensive. The decision-making kernel of the decision support system is implemented as a multi-agent-system (MAS). The kernel provides a distributed hierarchical algorithm for cooperative transportation planning and an on-line data layer that contains all the information for decision making. We sketch the distributed hierarchical transportation planning algorithm and identity the required software agents. The MAS interacts via web services with a commercial tour planning system that persistently stores the resulting tour plans, orders, and customer data. Moreover, the tour planning system is used to offer graphical user interfaces to interact with the users. The data layer is updated by order and customer data from the ERP systems of the different manufacturing companies. We describe the architecture and the implementation of the MAS and the overall coupling framework. Furthermore, we discuss the simulation-based performance assessment of the resulting decision support system when the system is applied in a rolling horizon setting and present some computational results. The results demonstrate that the MAS approach is appropriate for the cooperative transportation planning domain.     

Multi-agent Mediator architecture for distributed manufacturing

A generic Mediator architecture for distributed task planning and coordination has been developed using multi-agent paradigms. In this approach, agents function autonomously as independent computing processes, and dynamic virtual clusters coordinate the agent's activities and decision making. This coordination involves dynamically created coordination agents and resource agents concurrently. The Mediator architecture contains three levels of these coordination agents: the template mediator, the data-agent manager, and the active mediator. The template mediator is the top-level global coordinator. This agent contains both the templates and the cloning mechanism to create the successively lower-level agents. Task plans are decomposed successively into subtasks, which are allocated to dynamically created agent clusters coordinated through data-agent managers and active mediators. Coordination of agent activity takes place both among the clusters and within each cluster. The system dynamically adapts to evolving manufacturing tasks, with virtual agent clusters being created as needed, and destroyed when their tasks are completed. The mediator architecture and related mechanisms are demonstrated using an intelligent manufacturing scheduling application. Both the machines and the parts involved in this production system are considered as intelligent agents. These agents use a common language protocol based on the Knowledge Query Manipulation Language (KQML). The generic Mediator approach can be used for other distributed organizational systems beyond the intelligent manufacturing application it was originally developed for.     

A multi-agent architecture for control of AGV systems

Agent is an autonomous, computational entity that can be viewed as perceiving its environment and acting upon it. Agents are event-driven objects that can be integrated in automated manufacturing environments to control certain tasks. In this paper a set of agents (a multi-agent system) is introduced to control an automated manufacturing environment. The architecture includes functions at the manufacturing cell level, materials handling and transport level, and factory scheduling level. Communication between these agents is accomplished by using a relational database (blackboard system). The relational database also integrates the requirements of a manufacturing execution system within the multi-agent task structure, which is unique to this architecture. Manufacturing cell and scheduling agents have been previously described in the literature. Here we focus our attention on the functions of the agents of the transport system, which is composed of a set of AGVs.     

Connectivity-maintaining Consensus of Multi-agent Systems With Communication Management Based on Predictive Control Strategy

This paper studies the connectivity-maintaining consensus of multi-agent systems. Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption, a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved. In this paper, communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus. The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller, so there is more freedom to develop an appropriate control strategy for achieving consensus. For the multi-agent systems with this novel communication management, a predictive control based strategy is developed for achieving consensus. Simulation results indicate the effectiveness and advantages of our scheme.      

Towards agent systems engineering

The term “ ” has became a buzzword these days, not only in academic circles, but also in some computer magazines and application fields of computer science like manufacturing and logistics. Hence, there is a pressing need for a design methodology for multi-agent systems. Or even stronger, there is a demand for an agent engineering technology, which includes computer assistance during the design phases, for the validation of the designed system, and for the implementation of the multi-agent system as a physically distributed system. This status report will introduce the field of multi-agent systems and its design. Three prominent applications will serve as a first motivation of the theme. Based on the ideas of the agent examples a discussion on the term agent will serve as a first motivation of the theme. Based of the theme. Based on the ideas of the agent architecture of an agent, its communication capabilities, and planning in a multi-agent context. After this broad technical introduction the main topic of this work will be tackled. An introduction to five methodologies for the design of multi-agent systems will be given and a new one, the method, will be presented. Eventually a brief discussion about the multi-agent design approaches will be given, the missing correlations to the task definitions will be critizised, and further research directions will be proposed.     

A multi-agent approach to process planning and fabrication in distributed manufacturing

In the paper a multi-agent approach to the development of a distributed manufacturing architecture is presented. An essential building block introduced here is the virtual work system (VWS) which represents a manufacturing work system in the information space. The VWS is structured as an autonomous agent and is a constituent entity of an agent network. In the network dynamic clusters of cooperating agents are solving manufacturing tasks. A machining work system and its VWS is demonstrated in a case study. Its role in the agent communication network is discussed in a process planning and fabrication domain.     

Multi-agent modeling for solving profit based unit commitment problem

Profit based unit commitment problem (PBUC) from power system domain is a high-dimensional, mixed variables and complex problem due to its combinatorial nature. Many optimization techniques for solving PBUC exist in the literature. However, they are either parameter sensitive or computationally expensive. The quality of PBUC solution is important for a power generating company (GENCO) because this solution would be the basis for a good bidding strategy in the competitive deregulated power market. In this paper, the thermal generators of a GENCO is modeled as a system of intelligent agents in order to generate the best profit solution. A modeling for multi-agents is done by decomposing PBUC problem so that the profit maximization can be distributed among the agents. Six communication and negotiation stages are developed for agents that can explore the possibilities of profit maximization while respecting PBUC problem constraints. The proposed multi-agent modeling is tested for different systems having 10–100 thermal generators considering a day ahead scheduling. The results demonstrate the superiority of proposed multi-agent modeling for PBUC over the benchmark optimization techniques for generating the best profit solutions in substantially smaller computation time.     

Performance comparison and analysis of reactive and planning-based control architectures for manufacturing

Although numerous distributed architectures ranging from hierarchical to non-hierarchical (or heterarchical) have been proposed for the control of manufacturing systems, very little research has focused on quantitative comparisons of these architectures. In this paper, an objective comparison of two architectures, each required to control the same manufacturing cell, is presented. The objective of this work is to gain insight into the behaviour of alternative control architectures that will ultimately be used to determine the best control architecture for a given manufacturing system. In particular, this research focuses on the role of planning horizon in control architecture design to determine whether intelligent control agents should plan ahead or simply react to change in their environment.     

MVC模式在JADE系统开发中的应用

针对由于JADE(Java Agent Development Environment)对带图形界面的代理的支持程度不够,从而造成开发出来的系统结构混乱,维护困难等问题,采用MVC(Model-View-Control)设计模式进行开发,提出了一种新的图形界面Agent开发架构,即采用模型、界面和控制等功能模块相分离的方式来构造系统,以实现对多代理系统开发和维护的简化。该架构除了加强JADE系统对图形界面代理的支持外,新增了对多种消息并行处理的支持;并且结构灵活,易于扩展,可对现有的多代理系统进行少量改造来实现结构调整。