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.     

面向装配的多agent排产模型研究

拖期罚金带来的成本问题使中小规模离散制造企业在竞争中面临巨大压力.为此,采用多agent技术设计面向装配的三层排产框架模型;利用多agent的自治和协同,结合经验规则,将复杂的大规模排产逐层分解,转化为可用算法优化的子问题,再自底向上归并更新,迭代寻优完成生产排产,并可依计划执行情况进行动态调度.以合作企业凹印机历史订单数据进行排产仿真,结果表明,通过对设备空闲时间分布的有效管理,此三层模型能够有效解决订单拖期问题,对中小企业的成本控制具有重要意义.     

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 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.     

Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey

Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.     

An effective detailed operation scheduling in MES based on hybrid genetic algorithm

A detailed operation scheduling solution based on hybrid genetic algorithm is proposed and integrated with the manufacturing execution system (MES) for multi-objective scheduling. The constraints and influences from real-time production information collected by MES will all be considered in scheduling procedures. Each order can be scheduled forward or backward and the various constraints such as the ones from working calendar, processing capacity of manufacturing resources and the connection type between the operation and the previous operation will be obeyed in scheduling. A genetic algorithm is designed according to the features of the scheduling problem. Two methods of operation sequence (OS) initialization (named as OSIOP and ROSI) and three methods of manufacturing resource selection (named as RSAPT, RSWTB and RRS) are designed for population initialization. A variable neighborhood search is designed and implanted in the process of GA to improve the scheduling results. The experiments are made and the results have proved the feasibility of the hybrid GA. This scheduling solution is programmed in \(\hbox {C}^{\#}\) and applied to a commercial MES software successfully.     

A non-preemptive scheduling algorithm for soft real-time systems

Real-time systems are often designed using preemptive scheduling and worst-case execution time estimates to guarantee the execution of high priority tasks. There is, however, an interest in exploring non-preemptive scheduling models for real-time systems, particularly for soft real-time multimedia applications. In this paper, we propose a new algorithm that uses multiple scheduling strategies for efficient non-preemptive scheduling of tasks. Our goal is to improve the success ratio of the well-known Earliest Deadline First (EDF) approach when the load on the system is very high and to improve the overall performance in both underloaded and overloaded conditions. Our approach, known as group-EDF (gEDF) is based on dynamic grouping of tasks with deadlines that are very close to each other, and using Shortest Job First (SJF) technique to schedule tasks within the group. We will present results comparing gEDF with other real-time algorithms including, EDF, Best-effort, and Guarantee, by using randomly generated tasks with varying execution times, release times, deadlines and tolerance to missing deadlines, under varying workloads. We believe that grouping tasks dynamically with similar deadlines and utilizing a secondary criteria, such as minimizing the total execution time (or other metrics such as power or resource availability) for scheduling tasks within a group, can lead to new and more efficient real-time scheduling algorithms.     

Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data

Radio frequency identification (RFID) has been widely used in manufacturing field and created a ubiquitous production environment, where advanced production planning and scheduling (APS) might be enabled. Within such environment, APS usually requires standard operation times (SOTs) and dispatching rules which have been obtained from time studies or based on past experiences. Wide variations exist and frequently cause serious discrepancies in executing plans and schedules. This paper proposes a data mining approach to estimate realistic SOTs and unknown dispatching rules from RFID-enabled shopfloor production data. The approach is evaluated by real-world data from a collaborative company which has been used RFID technology for supporting its shopfloor production over seven years. The key impact factors on SOTs are quantitatively examined. A reference table with the mined precise and practical SOTs is established for typical operations and suitable dispatching rules are labled as managerial implicities, aiming at improving the quality and stability of production plans and schedules.     

A Decision Support System for a small scale industry in a developing country

This paper describes the design and development of a Decision Support System (DSS) for a small scale manufacturing company in a developing country. The DSS provides a solution for an integrated problem of planning, shopfloor scheduling, and control. The DSS system accepts the demand data with priorities, then uses the information from the imbedded data base system regarding product structure, production process, available resources with the status, and inventory. This in turn is used by the imbedded models to solve planning and scheduling problems, and provide solutions containing job order release, shopfloor scheduling and material requirement report. What if analysis feature provided by DSS allows the managers to incorporate their knowledge and experience to improve the quality of the solution by evaluating different scenarios presented by the manager. The laboratory equipment manufactured by the company is made to order. Product information regarding twenty-two high demand items is incorporated into the data base system, and the scheduling problem is solved for these items. The jobshop is a mixed shop problem containing a flow shop, an open shop, and a flow shop. A heuristic method used for solving this problem is presented.     

Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window

Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.     

Control latency for task assignment and scheduling of multiprocessor real-time control systems

For task assignment and scheduling problems of multiprocessor real-time control systems, a new performance index called the control latency, is proposed. In order to ensure smooth operation and good performance of real-time control systems, one must analyse the problem of combined task assignment and scheduling during the conceptual system design stage. The proposed performance index, the control latency, is defined as a weighted sum of feedback, command and monitoring latencies. Given a set of tasks for a specific control application, each task execution time and intra-/interprocessor communication latencies, an algorithm for combined task assignment and scheduling can be solved by minimizing this performance index, thereby providing the minimum time delay and best performance.     

Intelligent tracking in manufacturing

Dynamic scheduling is an important new innovation in manufacturing and supply chain management. However, the success of dynamic scheduling will depend on real-time information. This paper describes intelligent tracking technologies that provide real-time information throughout the supply chain to support keywords a logistics planning and execution.     

Preliminary design and manufacturing planning integration using web-based intelligent agents

Software agents have been increasingly used in the product and process development in industry over the past years due to the rapid evolvement of the Internet technology. This paper describes agents for the integration of conceptual design and process planning. Agents provide mechanisms to interact with each other. This mechanism is important since both of those processes involve negotiations for optimization. A set of design and planning software agents has been developed. These agents are used in a computer-based collaborative environment, called a multi-agent platform. The main purpose of developing such a platform is to support product preliminary design, optimize product form and structure, and reduce the manufacturing cost in the early design stage. The agents on the platform have access to a knowledge base that contains design and planning rules. These rules are derived from an analysis of design factors that influence process and resource planning, such as product material, form, shape complexity, features, dimension, tolerance, surface condition, production volume, and production rate. These rules are used by process planning agents to provide process planners with information regarding selecting preliminary manufacturing processes, determining manufacturing resources, and constructing feedback information to product designers. Additionally, the agents communicate with WEB servers, and they are accessible by users through Internet browsers. During performing design and planning tasks, agents access the data pertinent to design and manufacturing processes by the programming interfaces of existing computer-aided design (CAD) and manufacturing system. The agents are supported by a developed prototype agent platform. The agents and the platform enable the information exchange among agents, based on a previously developed integrated design and manufacturing process object model.     

A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems

In real manufacturing environments, the control of system elements such as automated guided vehicles has some difficulties when planning operations dynamically. Multi agent-based systems, a newly maturing area of distributed artificial intelligence, provide some effective mechanisms for the management of such dynamic operations in manufacturing environments. This paper proposes a multi-agent based scheduling approach for automated guided vehicles and machines within a manufacturing system. The proposed multi-agent based approach works under a real-time environment and generates feasible schedules using negotiation/bidding mechanisms between agents. This approach is tested on off-line scheduling problems from the literature. The results show that our approach is capable of generating good schedules in real time comparable with the optimization algorithms and the frequently used dispatching rules.     

Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks

In this note, we revisit the problem of scheduling stabilizing control tasks on embedded processors. We start from the paradigm that a real-time scheduler could be regarded as a feedback controller that decides which task is executed at any given instant. This controller has for objective guaranteeing that (control unrelated) software tasks meet their deadlines and that stabilizing control tasks asymptotically stabilize the plant. We investigate a simple event-triggered scheduler based on this feedback paradigm and show how it leads to guaranteed performance thus relaxing the more traditional periodic execution requirements.     

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。     

一种支持VPL的制造执行系统的体系结构

在制造执行系统(Manufacturing Execution Systems，MES）中实现VPL(Virtual Production Line)可以较好地平衡现代制造系统对生产效率和灵活性的需求。提出了基于ARC模型（Asent，Role，Character)的车间-工艺协作-资源(SVR)、支持VPL实现的多代理MES体系结构。给出了一个车间订单处理实例，说明了该系统实现高度灵活性的特点。     

智能协同集成逆向工程系统控制模型的研究

为提高集成逆向工程系统的智能和协同能力,提出了基于MAS的控制模型。该模型分为管理智能体层、单元智能体层和执行智能体层。管理智能体层负责调度和协调各CA,并对所有CA进行管理;单元智能体层中的各CA间通过协商承担相应的任务,完成任务的一级分配;执行智能体对逆向系统内的硬件、软件负责,它们根据局部的本地资源信息及当前状态,接收发布的任务,并对其求解。同一层次的CA之间采用分布式结构,对等协作;上下级的CA间采用主从协作。     

A new model of scheduling in manufacturing: tasks, roles, and monitoring

For over 3 decades there was a belief that computer-based solutions would "solve" complex industrial scheduling problems, yet most manufacturing organizations still require human contributions for effective scheduling performance. We present a new model of scheduling for the development and implementation of effective scheduling systems within manufacturing companies. The model derives from investigating the work of 7 schedulers in 4 manufacturing environments using a qualitative field study approach, for which novel field-based data collection and analysis methods were developed. The results show that scheduling in practice comprises task, role, and monitoring activities and that the business environment influences a scheduler at work. A new definition of scheduling is presented that includes the significant facilitation and implementation aspects of human scheduling ignored by many computer-based scheduling approaches. The implications for this model extend across the domains of human factors and operations management, especially for the analysis and improvement of existing and new production planning and control processes and enterprise information systems. Actual or potential applications of this research include the analysis, design, and management of planning, scheduling, and control processes in industry; the selection, training, and support of production schedulers; and the allocation of tasks to humans and computer systems in industrial planning, scheduling, and control processes.     

一种适用于Internet的实时多Agent系统模型

提出了一种基于Agent to Agent架构的,适用于Internet环境的实时多Agent系统模型。重点阐述了该模型中Agent之间的协作方式、调度策略和任务执行的控制策略,并介绍了采用的实时Agent通信语言和通信机制。