Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology

Integrating physical objects with the corresponding enterprise applications any time any where is the essential issue for a real-time enterprise. This study proposes a multi-agent system framework called agent-based manufacturing control and coordination (AMCC) system, a agent-based framework using ontology and RFID technology to monitor and control dynamic production flows and also to improve the traceability and visibility of mass customization manufacturing processes. The capabilities offered by multi-agent systems to respond to RFID events in real-time and a broad class of agent design and coordination issue regarding just in time (JIT) and just in sequence (JIS) manufacturing processes are also exploited in this study. To validate the proposed framework, case study of a bicycle manufacturing company is used to demonstrate how the proposed framework can benefit its JIT production. Finally, an example prototype system is implemented to demonstrate the concept of the proposed framework.     

Agent-based Gateway Operating System for RFID-enabled ubiquitous manufacturing enterprise

Capturing and processing of real-time manufacturing shop floor field data is essential in improving the performance of shop floor planning, execution and control. Radio Frequency IDentification (RFID) has enabled real-time information visibility and realized ubiquitous manufacturing enterprises with proper functionalities of Enterprise Information Systems (EISs). This paper presents a flexible, modularized and re-configurable framework for the new generation RFID middleware system, named Gateway Operating System (GOS). It is an overall software solution designed and proposed not only to address basic functions of RFID middleware system, but also to overcome the particular challenges and requirements for real-life manufacturing scenarios. GOS aims to provide an easy-to-deploy, simple-to-use and affordable RFID middleware solution for manufacturing applications. A multi-agent based model, named gateway smart agent manager, is designed to enable the heterogeneous RFID devices in a “Plug and Play” fashion and to cope with the changes from these connected hardware devices. To guarantee the versatility and scalability of GOS, an XML (eXtensible Markup Language) based message exchanging protocol is designed to fulfill the communication and interactions between applications and devices. Based on this protocol, an easy-to-deploy and simple-to-use application manager is built to manage, configure and use the connected devices as well as deployed applications. The proposed GOS will provide a new referenced framework for the development of lightweight RFID middleware system for manufacturing environment.     

基于多Agent的I-MES在烟草企业中的实现

本文以面向敏捷企业的智能制造执行系统(I-MES)为研究对象,采用多Agent技术作为系统的实现形式,对系统的多Agent组织结构、系统中多Agent间的通信和协作机制以及多Agent的基于事件和周期的动态调度策略等理论问题和关键技术进行了研究,针对可集成制造执行系统中最常见的资源冲突问题,提出了一种基于多Agent合作的冲突消解模型;在此基础上针对敏捷环境下传统的制造执行系统的不足,以烟草企业这类流程性工艺企业为背景,基于多代理技术,构建了一个基于多代理具有开放性、集成性、动态性、敏捷性和可互操作的敏捷制造智能执行系统,     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

RFID together with multi-agent systems to control global value chains

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.     

Multi-agent based real-time production scheduling method for radio frequency identification enabled ubiquitous shopfloor environment

The lack of timely feedback shopfloor information during manufacturing execution stage leads to significant difficulties in achieving real-time production scheduling. To address this problem, an overall architecture of multi-agent based real-time production scheduling is presented to close the loop of production planning and control. Several contributions are significant. Firstly, wireless devices such as radio frequency identification (RFID) are deployed into value-adding points in a ubiquitous shopfloor environment to form Machine Agent for the collection and processing of real-time shopfloor data. Secondly, Capability Evaluation Agent is designed to optimally assign the tasks to the involved machines at the process planning stage based on the real-time utilization ration of each machine. The third contribution is a Real-time Scheduling Agent for manufacturing tasks scheduling/re-scheduling strategy and methods according to the real-time feedback. Fourthly, a Process Monitor Agent model is designed for tracking and tracing the manufacturing execution based on a critical event structure. Finally, a case is used to demonstrate the proposed multi-agent based real-time production scheduling models and methods.     

A multi-agent system to construct production orders by employing an expert system and a neural network

The authors describe the implementation of a multi-agent system, whose goal is to enhance production planning i.e. to improve the construction of production orders. This task has been carried out traditionally by the module known as production activity control (PAC). However, classic PAC systems lack adaptive techniques and intelligent behaviour. As a result they are mostly unfit to handle the NP Hard combinatorial problem underlying the construction of right production orders. To overcome this situation, we illustrate how an intelligent and collaborative multi-agent system (MAS) obtains a correct production order by coordinating two different techniques to emulate intelligence. One technique is performed by a feed-forward neural network (FANN), which is embedded in a machine agent, the objective being to determine the appropriate machine in order to fulfil clients’ requirements. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The entire MAS consists of a coordinator, a spy, and a scheduler. The coordinator agent has the responsibility to control the flow of messages among the agents, whereas the spy agent is constantly reading the Enterprise Information System. The scheduler agent programs the production orders. We achieve a realistic MAS that fully automates the construction and dispatch of valid production orders in a factory dedicated to produce labels.     

基于MAS的动态生产调度与控制及系统开发

提出基于MAS的面向敏捷制造的生产过程动态调度与控制的层次结构．1）以任务分解与分配层为中心，建立各层之间的协调工作及协同决策机制；2）引入协商式招／投标方法实现任务的分解与分配；3）采用能力匹配与动态调度相结合的方法实现任务分配与调度控制的有效集成；4）面向生产任务需求动态确定Agent粒度、组建MAS模型；5）适应制造系统状态变化的需要，进行任务的动态重构．讨论基于MAS的采用分级递阶和并行处理相结合的自治组织结构和运作模式，以及利用与组织结构相对应的层次黑板结构实现各Agent之间信息与数据共享．在支持生产过程动态调度与控制基础设施建设的基础上，结合奏川机床集团有限公司车间生产实际，研究开发了基于MAS的车间动态调度系统．     

Agility and production flow layouts: An analytical decision analysis

The term ‘agile manufacturing’ has referred to operational aspects of a manufacturing company concerning their ability to produce customized products at mass production prices and with short lead times. A core issue faced within agile manufacturing is the need for appropriate and supporting production and operations systems. Many design dimensions of agility and agile manufacturing exist. To help attain this goal for integrating the many design dimensions, operations infrastructure and capacity must be carefully planned to manage production flow, and thus production layout planning takes on an increasingly important role. Given the importance of these dimensions in response to agility, this paper seeks to make a contribution by providing insights into a decision aid for evaluating production flow layouts that support and enhance the agile manufacture of products. Layout design has a significant impact on the performance of a manufacturing or service industry system and has been an active research area for many decades. Strategic evaluation of production layouts requires consideration of both qualitative and quantitative factors (managerial, organizational, and technical). This paper makes use of the Analytical Network Process (ANP) which captures interdependencies among different criteria, sub-criteria and dimensions, an evident characteristic of production flow layouts in complex agile manufacturing environments. An application case study exemplifying the practical usefulness of this type of model describes how management, after implementation of the model, made a mid-course correction related to the production layout initially selected.     

基于模型重构的生产计划优化系统设计与开发

敏捷制造是一种可以快速响应客户需求的制造模式,但现有敏捷制造模式下的生产计划优化系统存在针对性弱、生产模型单一等问题。文中通过提取组成生产目标规划模型的各种因子,对敏捷制造模式下满足不同生产目标和生产条件的生产模型进行了重构,并采用卡马卡及其关联预测算法进行了模型求解,从而大大提高了车间生产计划优化系统的灵活性和适用性,为生产效率的提高提供了一条更加科学有效的途径。     

敏捷供应链协同决策体系中MAS的结构与设计

根据敏捷供应链管理过程中决策活动的特点，阐述了敏捷供应链协同决策体系中引入Agent的原因，讨论了多Agent系统(MAS)在敏捷供应链协同决策体系中的应用，研究了MAS的组成结构，建立了敏捷供应链协同决策体系中上层Agent联邦的功能组成，并提出了一种基于Java bean组件技术和面向Agent设计的MAS的设计方法。最后，介绍了MAS在敏捷供应链协同商务系统中的应用。     

Structured intelligence for self-organizing manufacturing systems

This paper deals with fuzzy scheduling and path planning problems by genetic algorithms. We have proposed a self-organizing manufacturing system (SOMS) that is composed of a number of autonomous modules. Each module decides output through interaction with other modules, but the module does not share complete information concerning other modules in the SOMS. Therefore, we require structured intelligence as a whole system. In this paper, we consider a manufacturing line composed of machining centres and conveyor units. The manufacturing procedure can be divided into a sequence of three modules: (a) tool locating module, (b) scheduling module, and (c) path planning module. The tool locating problems have been already solved. In this paper, we first solve the scheduling problem as global preplanning. Here we assume that the processing time is not constant, because some delay may occur in the machining centres. We therefore apply fuzzy theory to represent incomplete information abou t the machining time. We solve the fuzzy scheduling problem with a genetic algorithm. After global preplanning, the path planning module transports materials and products. Next, the scheduling module acquires the actual processing time of each machining centre. Based on the processing time, the schedule module generates a fuzzy number for the processing time. We discuss the effectiveness of the proposed method through the computer simulation results.     

基于多Agent的可重构制造系统集成模型

应用控制、管理和维护一体化的自动化技术,建立了基于多Agent的可重构制造系统RMS（Reconfigurable Manufacturing System）集成模型。该模型集成了基于多Agent 的RMS重构模型、控制模型和故障诊断模型,将RMS的控制、管理和维护联系起来,并给出了该模型的UML（Unified Modeling Langurage）活动图,最后举例验证了模型的可行性。     

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.     

Manufacturing planning and predictive process model integration using software agents

Intelligent agents provide a means to integrate various manufacturing software applications. The agents are typically executed in a computer-based collaborative environment, referred to as a multi-agent system. The National Institute of Standards and Technology (NIST) has developed a prototype multi-agent system supporting the integration of manufacturing planning, predictive machining models, and manufacturing control. The agents within this platform have access to a knowledge base, a manufacturing resource database, a numerical control programming system, a mathematical equation solving system, and a computer-aided design system. Intelligence has been implemented within the agents in rules that are used for process planning, service selection, and job execution. The primary purposes for developing such a platform are to support the integration of predictive models, process planning, and shop floor machining activities and to demonstrate an integration framework to enable the use of machining process knowledge with higher-level manufacturing applications.     

Modeling of agile intelligent manufacturing-oriented production scheduling system

Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.     

Architectural requirements for rapid development of agile manufacturing systems

As product life cycle becomes shortened, high product quality becomes necessary for survival, and continuous and unexpected change becomes key obstacles in success, the need for a method of rapidly and cost-effectively developing products, production facilities and supporting software including design, process planning, shop floor control systems becomes urgent. The essence of this concept of manufacturing would be characterized by adopting a new term “agility”. Agile manufacturing can be successfully accomplished using various well-defined system architecture. This paper provides a primary sketch of architectural requirements for rapid development of agile manufacturing systems.There are several aspects of system architecture : control, function, process, information, communication, distribution, development, and implementation.In the past, the confusion of those architectures prohibited the successful construction of the automated CIM systems.     

多agent规划综述*

多Agent规划是智能规划和多Agent系统的交叉领域,随着智能规划领域研究范围的不断扩展和多Agent系统领域研究内容的不断深入,多Agent规划受到了越来越多的关注。有鉴于此,本文对多Agent规划的概念和主要方法进行全面综述。具体内容包括智能规划和多Agent系统的背景介绍、多Agent规划的各种形式化描述方式以及基于规划修复、合并或者马尔可夫决策过程的的分布式规划方法。最后,还给出了多Agent规划的发展趋势。     

Agile MPC system linking manufacturing and market strategies

Increasing complexity and interdependency in manufacturing enterprises require an agile manufacturing paradigm. This paper considers a dynamic control approach for linking manufacturing strategy with market strategy through a reconfigurable manufacturing planning and control (MPC) system to support agility in this context. A comprehensive MPC model capable of adopting different MPC strategies through distributed controllers of inventory, capacity, and WIP is presented. A hierarchical supervisory controller (referred to as decision logic unit, DLU) that intakes the high-level strategic market decisions and constraints together with feedback of the current manufacturing system state (WIP, production, and inventory levels) and optimally manages the distributed controllers is introduced. The DLU architecture with its three layers and their different functionalities is discussed showing how they link the higher management level to the operational level to satisfy the required demand. A case study for an automatic PCB assembly factory is implemented to demonstrate the applicability of the whole approach. In addition, a comparative cost analysis study is carried out to compare between the developed agile MPC system and classical-inventory- and capacity-based MPC policies in response to different demand patterns. Results showed that the developed agile MPC policy is as cost effective as the inventory-based MPC policy in demand patterns with steady trends, as cost effective as capacity-based MPC in turbulent demand patterns, and far superior than both classical MPC polices in mixed-demand patterns.