Internet-based manufacturing: A review and a new infrastructure for distributed intelligent manufacturing

Global competition and rapidly changing customer requirements are forcing major changes in the production styles and configuration of manufacturing enterprises. Traditional centralized manufacturing systems are not able to meet these requirements. In recent years, the internet has become the worldwide information platform for the sharing of information and data. Information processing is an important challenge in an internet-based manufacturing environment, and must facilitate distribution, heterogeneity, autonomy and cooperation. This paper reviews recent trends and major developments in internet-based manufacturing. Broad categories of distributed information management are identified, based upon the development of computer integrated manufacturing, and the possible direction of future research is indicated.     

Enhancing the performance of an agent-based manufacturing system through learning and forecasting

Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through learning from history (based on distributed case-based learning and reasoning) and learning from the future (through system forecasting simulation). Learning from history is used to enhance coordination capabilities by minimizing communication and processing overheads. Learning from the future is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.     

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.     

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 CORBA-based agent-driven design for distributed intelligent manufacturing systems

An agent-oriented methodology is presented for representation, acquisition, and processing of manufacturing knowledge along with analysis and modeling of an intelligent manufacturing system (IMS). An intelligent manufacturing system adopts heterarchical and collaborative control as its information system architecture. The behavior of the entire manufacturing system is collaboratively determined by many interacting subsystems that may have their own independent interests, values, and modes of operation. The subsystems are represented as agents. An agent's architecture and task decomposition method are presented. The agent-oriented methodology is used to analyze and model an intelligent machine cell. An intelligent machine center is considered as an autonomous, modular, reconfigurable and fault-tolerant machine tool with self-perception, decision making, and self-process planning, able to cooperate with other machines through communication. The common object request broker architecture (CORBA) distributed software control system was developed as a simple prototype. A case study illustrates an intelligent machine center.     

Expert systems for manufacturing cell simulation and design

Computer Integrated Manufacturing (CIM) is approached by means of the application of Computer Aided Design (CAD), Computer Aided Manufacturing (CAM) and other CA techniques, methods and programs/program systems. These programs are often implemented as knowledge-based, or expert systems and in this way they became typical examples of engineering application of artificial intelligence. The production task of CIM systems is solved by using flexible manufacturing systems (FMSs). FMSs built up from smaller, complex units, i.e. from flexible manufacturing cells (FMCs) have several advantages. The design and the operation of manufacturing systems need new, sophisticated methods to utilize all the embedded benefits of the sophisticated and expensive elements installed for production purposes. New methods like knowledge processing technology, cooperative problem-solving techniques, etc., offer wide possibilities to design more reasonable systems. This paper describes prototype expert systems that make use of different knowledge-based tools and techniques to design (configure, reconfigure) and simulate manufacturing cells, taking into consideration technological plans and other relevant information.     

An object-oriented framework for developing distributed manufacturing architectures

Management of complexity, changes and disturbances is one of the key issues of production today. Distributed, agent-based structures represent viable alternatives to hierarchical systems provided with reactive/proactive capabilities. In the paper, approaches to distributed manufacturing architectures are surveyed, and their fundamental features are highlighted, together with the main questions to be answered while designing new structures. Moreover, an object-oriented simulation framework for development and evaluation of multi-agent manufacturing architectures is introduced.     

Integrated distributed intelligent system for process-control system design

In process-control system design, the Computer Aided Design (CAD) techniques have evolved a new generation of design techniques. This also paves the way for implementing Computer Integrated Manufacturing Systems (CIMS). However, the key issue to accomplishing the objectives in CAD is the automation of conceptual design. This paper first introduces the fundamentals of conceptual design for process-control system design. Then, a strategy to solve the problems in conceptual design is proposed. The associated system configuration and software implementation platform for developing intelligent systems for process control system design are presented. Finally, an application case is studied.     

An organism-oriented modeling approach to support the analysis and design of intelligent manufacturing systems

In this paper a generic macroscopic object, termed an organism, is introduced. An organism defines a high-level modeling object that has the capabilities for organizational networking, standardization or characteristics specifications, decoupling of editing and visualization as well as temporal management. Organism-oriented models inherit from their parent object-oriented and object/agent-oriented models a simplified representation of the manufacturing entities as well as a capacity for many levels of abstraction. Moreover, the organism-oriented modeling approach enriches these models by not only considering basic manufacturing objects and agents, but also the fact that each of these objects and agents may itself be an organization and also part of one or several organizations. The paper first surveys some of the current approaches used for modeling and analysing manufacturing systems: structured analysis, Petri nets, object and object/ agent models. The object model behind the organism-oriented modeling approach is then presented and its application to a manufacturing case is illustrated.     

Fuzzy neural networks for intelligent design retrieval using associative manufacturing features

In the conceptual design stage, designers usually initiate a design concept through an association activity. The activity helps designers collect and retrieve reference information regarding a current design subject instead of starting from scratch. By modifying previous designs, designers can create a new design in a much shorter time. To computerize this process, this paper proposes an intelligent design retrieval system involving soft computing techniques for both feature and object association functions. A feature association method that utilizes fuzzy relation and fuzzy composition is developed to increase the searching spectrum. In the mean time, object association functions composed by a fuzzy neural network allow designers to control the similarity of retrieved designs. Our implementation result shows that the intelligent design retrieval system with two soft computing based association functions can retrieve target reference designs as expected.     

An architecture for a self-improving instructional planner for intelligent tutoring systems

Machine instructional planners use changing and uncertain data to incrementally configure plans and control the execution and dynamic refinement of these plans. Current instructional planners cannot adequately plan, replan, and monitor the delivery of instruction. This is due in part to the fact that current instructional planners are incapable of planning in a global context, developing competing plans in parallel, monitoring their planning behavior, and dynamically adapting their control behavior. In response to these and other deficiencies of instructional planners a generic system architecture based on the blackboard model was implemented. This self-improving instructional planner (SUP) dynamically creates instructional plans, requests execution of these plans, replans, and improves its planning behavior based on a student's responses to tutoring. Global planning was facilitated by explicitly representing decisions about past, current, and future plans on a global data structure called the plan blackboard. Planning in multiple worlds is facilitated by labeling plan decisions by the context in which they were generated. Plan monitoring was implemented as a set of monitoring knowledge sources. The flexible control capability for instructional planner was adapted from the blackboard architecture BB1. The explicit control structure of SUP enabled complex and flexible planning behavior while maintaining a simple planning architecture.     

Dynamic hierarchical control for distributed problem solving

Distributed problem solving (DPS) has become one of the central topics in AI. Much research has been concerned with finding an appropriate distributed control regime. We propose the concept of dynamic, hierarchical control (DHC) for distributed problem solving.

In many domains, DPS has a natural hierarchy or a subproblem hierarchy can be imposed by utilizing appropriate decomposition techniques. DHC aims at exploiting the power inherent in the hierarchical approach. It also enables the control of the problem solving process to fit the structure of a domain. This results in well-coordinated cooperation and coherent negotiation among distributed controllers.

We have used the DHC to perform plant combine production planning (PCPP). This task involves the activity of a network of production units, each with possibly different characteristics, collaborating to produce various items under dynamically changing conditions. We also describe the general structure of a single controller which adopts a blackboard and a knowledge source (KS) scheduling mechanism to carry out dynamic process execution.

The paper describes the results of our first solution to this problem. Finally, we discuss on-going research that aims at handling additional problems.     

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.     

Model of an integrated intelligent design and manufacturing system

The expert system STATEXS is presented for dimensioning, optimization and manufacture of gears and gearings. The optimum dimensions of the gearing were determined using genetic algorithms, well suited to such problems especially because of their robustness and their ability to detect global extremes. After completion of the calculations and optimization of the gears or gear pairs, there follows one of the most difficult operations, the manufacture of the product with theoretically determined and optimized properties. To this end we have also started to use the genetic algorithm approach for the manufacture of various products with demanding shapes.     

基于Agent分布式仪表产品造型协同设计应用研究

基于Agent代理,提出将Web技术和Multi-Agent技术融合的分布式协同设计系统,为产品设计过程中设计人员之间的信息共享和资源共享提供了更有效的方法.在分析系统体系结构的基础上,着重阐述了Multi-Agent代理模型和工作原理,并将其应用于仪表产品造型设计系统中,实现基于Agent的分布式仪表产品造型设计原型系统.     

Automatic verification of distributed systems: The process algebra approach

A survey of tools for the analysis of distributed systems represented through process algebras is presented. The tools are compared with respect to a set of qualitative parameters. From this analysis, the properties which are desirable for concurrency tools are investigated. Criteria to evaluate the suitability of a tool with respect to a particular user are proposed.     

Expert systems in production planning and scheduling: A state-of-the-art survey

Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Especially the last decade has witnessed a growing number of manufacturing companies, including glass, oil, aerospace, computers, electronics, metal and chemical industries—to name just a few—interested in the applications of expert systems (ESs) in manufacturing. This paper is a state-of-the-art review of the use of ESs in the field of production planning and scheduling. The paper presents famous expert systems known in the literature and current applications, analyzes the relative benefits and concludes by sharing thoughts and estimations on ESs future prospects in this area.     

Petri net based process scheduling: A model of the control system of flexible manufacturing systems

In this paper, we propose a class of algorithms for the sub-optimal solution of a particular class of problems of process scheduling, particularly focusing on a case study in the area of flexible manufacturing systems (FMSs). The general class of problems we face in our approach is characterized as follows: there is a set of concurrent processes, each formed by a number of temporally related tasks (segments). Tasks are executable by alternate resource sets, different both in performance and costs. Processes and tasks are characterized by release times, due dates, and deadlines. Time constraints are also present in the availability of each resource in resource sets. It has been proven that such a problem does not admit an algorithm for an optimal solution in polynomial time. Our proposed algorithm finds a sub-optimal schedule according to a set of optimization criteria, based on task and process times (earliness, tardiness), and/or time independent costs of resources. Our approach to process scheduling is based on Timed Coloured Petri Nets. We describe the structure of the coordination and scheduling algorithms, concentrating on (i) the general-purpose component, and (ii) the application-dependent component. In particular, the paper focuses on the following issues: (i) theautomatic synthesis of Petri net models of the coordination subsystem, starting from the problem knowledge base; (ii) the dynamic behavior of the coordination subsystem, whose kernel is a High Level Petri net executor, a coordination process based on an original, general purpose algorithm; (iii) the structure of the real-time scheduling subsystem, based on particular heuristic sub-optimal multi-criteria algorithms. Furthermore, the paper defines the interaction mechanisms between the coordination and scheduling subsystems. Our approach clearly distinguishes the mechanism of the net execution from the decision support system. Two conceptually distinct levels, which correspond to two different, interacting implementation modules in the prototype CASE tool, have been defined: theexecutor and thescheduler levels. One of the outstanding differences between these levels is that the executor is conceived as a fast, efficient coordination process, without special-purpose problem-solving capabilities in case of conflicts. The scheduler, on the other hand, is the adaptive, distributed component, whose behavior may heavily depend on the problem class. If the scheduler fails, the executor is, in any case, able to proceed with a general-purpose conflict resolution strategy. Experimental results on the real-time performance of the kernel of the implemented system are finally shown in the paper. The approach described in this paper is at the basis of a joint project with industrial partners for the development of a CASE tool for the simulation of blast furnaces.     

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.