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.     

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 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.     

Using multi-agent architecture in FMS for dynamic scheduling

The proposed scheduling strategy is based on a multi-agent architecture. Each agent of this architecture is dedicated to a work centre (i.e. a set of resources of the manufacturing system); it selects locally and dynamically the most suitable dispatching rules. Depending on local and global considerations, a new selection is carried out each time a predefined event occurs (for example, a machine becomes available, or a machine breaks down). The selection depends on: (1) primary and secondary performance objectives, (2) the operating conditions, and (3) an analysis of the system state, which aims to detect particular symptoms from the values of certain system variables. We explain how the scheduling strategy is shared out between agents, how each agent performs a local dynamic scheduling by selecting an adequate dispatching rule, and how agents can coordinate their actions to perform a global dynamic scheduling of the manufacturing system. Each agent can be implemented through object-oriented formalisms. The selection method is improved through the optimization of the numerical thresholds used in the detection of symptoms. This approach is compared with the use of SPT, SIX, MOD, CEXSPT and CR/SPT on a jobshop problem, already used in other research works. The results indicate significant improvements.     

Reactive scheduling using a multi-agent model: the SCEP framework

Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, and it permits a natural co-habitation between infinite capacity scheduling processes, performed by the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop.     

A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA–SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.     

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.     

A re-configurable multi-agent system architecture for error recovery in production systems

Multi-agent systems for manufacturing systems appear to provide adequate response to abrupt disturbances on the shop floor. To date, most of the work has been focused on planning and scheduling but very little work has been done on issues pertaining to monitoring, diagnostics and error recovery. Our approach addresses the issue of combining the discipline of hierarchical systems with the agility of multi-agent systems. Within the context of a hierarchy, the focus is on the workstation level and, in particular, the construction of a re-configurable system having production agents, error recovery agents, and a mediator agent structure connecting production and recovery agent hierarchies. In addition, the relationship to a multi-level, multi-layer hierarchy control is established. This latter hierarchy, based on Petri Net constructs, serves, in one sense, as a retrieval based resource for process planning and generation of recovery plans for production and recovery agents within the proposed multi-agent system. An objective of this effort is to provide a test-bed for comparison of hierarchical systems, heterarchical, and a hybrid combination which is the focus of the investigation presented here.     

Manufacturing system application of a knowledge based simulation

The flexibility in a modern manufacturing facility introduces complexity to the decision making. The requirement for a real time control mechanism results in a need for an on-line simulation system that can simulate the complicated scheduling, routing and similar decisions required by the system.

This paper describes a knowledge-based simulation and control system written in PROLOG. This system is composed of four parts: a static data base, a dynamic database, a modeling knowledge part, and a simulation driver. The simulator contains the actual control mechanism of the physical system and can be removed from the system in order to simulate it.

The manufacturing system under investigation is a combination of flexible manufacturing cells that feed an automated assembly station. The knowledge-based simulation helps to develp and understand control mechanisms for that system, which can later be implemented in the physical system as the controller.     

Design and implementation of a relational data base for automated process planning

The function of process planning in a manufacturing facility is to identify the processes and the machines required to convert a design into its final form. Majority of the process plans are still made manually. The degree of the process plan detail varies with the shop environment. A feature-based process planning system which interacts with the SDRC I-DEAS solid modeling software is presented. The system operates under the UNIX environment. A relational database model which incorporates manufacturing details including the operation selection, machine selection, tool selection, fixture selection, etc. was developed and implemented. This relational database was structured using Microsoft SQL SERVER for the PC multi-user environment. This database is part of an ongoing research project on automated process planning and scheduling and primarily focuses on prismatic parts. The developed routines generate the Overall Removable Volume (ORV) graphically by comparing the desirable design to the blank work piece. The routines then decompose the ORV into various General Manufacturing Features (GMF) based on concave edges. The recognition routine captures geometrical and topological information and identifies the shape of each GMF. An expert system (using CLIPS expert system shell) analyzes the GMFs and determines appropriate operation(s) for the designed part. The developed expert system is capable of recommending the suitable machines, tools, fixtures, etc.     

The model-based product agent: A control oriented architecture for intelligent products in multi-agent manufacturing systems

The multi-agent control strategy has been previously shown to improve the flexibility of complex, dynamic manufacturing systems. One key component of this strategy is the product agent. The product agent is responsible for autonomously guiding a physical part in the manufacturing system based on its production goals. Though the product agent has been described in previous works, a fully developed software architecture for the product agent that uses a model-based optimization approach has not been proposed. In this work, a product agent architecture with the capabilities to explore the local environment, plan and schedule events based on its knowledge, and request desired actions from the resources in the system is presented and tested.     

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.     

一个基于JaVa的多Agent系统框架JAFMAS及其改进

介绍一种基于Java多Agent系统构造框架JAFMAS。该框架可为多Agent系统的开发者提供了一组可扩展的Java类,使得开发者将力量集中于Agent的具体功能细节而不必担心Agent的通信方法和行为协调机制。该文介绍了框架的体系结构和分布式环境下Agent间的通信方法和行为协调机制,同时对JAFMAS做了一些改进。     

流程工业CIMS中的实时数据库技术

流程工业存在着大量的实时数据处理、存储和集成问题,仅靠采用集散式控制系统（ＤＣＳ）和关系数据库技术并不能完全解决。开放结构的分布式实时数据库系统能够提供高速的实时数据服务,能够有效地集成异构控制系统,它和关系数据库一起构成了流程企业的数据平台,对流程工业的生产信息集成起着极其重要的作用,是流程工业ＣＩＭＳ实施中的关键技术。该文针对流程工业ＣＩＭＳ的特点,介绍了在流程工业ＣＩＭＳ中实时数据库的功能．系统结构及其采用的关键技术。     

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.     

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.     

多智能体车间调度系统研究

现在制造业所面临的动态需求使得其必须具有更加灵活的应变机制，这使得车间调度问题变得越来越复杂。本文采取多智能体系统技术（MAS）设计了一个包含四个智能体（agent）的多智能体车间调度系统，分别为车间调度智能体、任务分配智能体、车间资源智能体以及拍卖智能体。通过这四个智能体的通信、交互和合作，系统可以给出一个满足当前制造需求的调度最优结果。     

Intelligent agent systems for manufacturing applications

Using the concept of intelligent agents, two system approaches are described for manufacturing applications. The first is based on the rule-based object (RBO) which is associated with a production-rules knowledge base and is implementable in an object-oriented development environment such as Smalltalk-80. A proof-of-concept automated guided vehicle (AGV) system of this kind has been recently developed. The second and more advanced approach is based on the intelligent agent object (IAO) and an AGV system of this kind is currently under development. This uses a multi-agent entity termed the modulon to handle more effectively both the co-operative planning task and the local route planning task.     

A novel approach for multi-agent-based Intelligent Manufacturing System

In the recent years, the competition of shortening the development cycle of new products is more and more fierce. Given the shortcomings of traditional scheduling algorithm in Intelligent Manufacturing, the architecture of multi-agent-based Intelligent Manufacturing System is put forward, which represents the basic processing entity. The architecture is based on the methodology of multi-agent systems (MAS) in distributed artificial intelligence (DAI). The multi-agent system has some common characteristics, such as distribution, autonomy, interaction and openness, which are helpful to transform the traditional architecture into a distributed and cooperative architecture in an Intelligent Manufacturing System. 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. 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.     

分布式多媒体DBMS的研究与开发

本文介绍分布式多式 媒体数据库管理系统－ＤＭ２。它基于作者提出的层次－关系数据模型，采用客户－服务器体系结构、多线索调度等一系列当代先进的软件技术，从而为用户建立多媒体ＭＩＳ提供了一个良好的开发环境。