System Thoughts in Manufacturing

In this paper we present a taxonomy of manufacturing problems, labeled in a general sense as Design, Production, or Distribution problems. One or more basic systems concepts, such as complexity and adaptation, attach themselves to each such problems. By combining the hierarchical Design—Production—Distribution idea with system concepts, we establish the fact that there is, indeed, a significant systems component to most problems of modern manufacturing.     

MetrIntPair—A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

In this paper, we propose a novel metric called MetrIntPair (Metric for Pairwise Intelligence Comparison of Agent‐Based Systems) for comparison of two cooperative multiagent systems problem‐solving intelligence. MetrIntPair is able to make an accurate comparison by taking into consideration the variability in intelligence in problem‐solving. The metric could treat the outlier intelligence indicators, intelligence measures that are statistically different from those others. For evaluation of the proposed metric, we realized a case study for two cooperative multiagent systems applied for solving a class of NP‐hard problems. The results of the case study proved that the small difference in the measured intelligence of the multiagent systems is the consequence of the variability. There is no statistical difference between the intelligence quotients/level of the multiagent systems. Both multiagent systems should be classified in the same intelligence class.     

Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance

Scheduling of single machine in manufacturing systems is especially complex when the order arrivals are dynamic. The complexity of the problem increases by considering the sequence-dependent setup times and machine maintenance in dynamic manufacturing environment. Computational experiments in literature showed that even solving the static single machine scheduling problem without considering regular maintenance activities is NP-hard. Multi-agent systems, a branch of artificial intelligence provide a new alternative way for solving dynamic and complex problems. In this paper a collaborative multi-agent based optimization method is proposed for single machine scheduling problem with sequence-dependent setup times and maintenance constraints. The problem is solved under the condition of both regular and irregular maintenance activities. The solutions of multi-agent based approach are compared with some static single machine scheduling problem sets which are available in the literature. The method is also tested under real-time manufacturing environment where computational time plays a critical role during decision making process.     

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.     

Crowd intelligence in AI 2.0 era

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence (AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-to-offline (O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.     

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.     

Network model and effective evolutionary approach for AGV dispatching in manufacturing system

Automated guided vehicles (AGVs), are the state-of-the-art, and are often used to facilitate automatic storage and retrieval systems (AS/RS). In this paper, we focus on the dispatching of AGVs in a flexible manufacturing system (FMS). A FMS environment requires a flexible and adaptable material handling system. We model an AGV system by using network structure. This network model of an AGV dispatching has simplexes decision variables with considering most AGV problem’s constraints, for example capacity of AGVs, precedence constraints among the processes, deadlock control. Furthermore, these problems can be solved by using a lot of heuristic algorithms as network optimization problems. We are also proposed an effective evolutionary approach for solving a kind of AGV’s problems in which minimizing time required to complete all jobs (i.e. makespan) and minimizing the number of AGVs, simultaneously. For applying an evolutionary approach for this multicriteria case of AGV problem, priority-based encoding method and Interactive Adaptive-weight GA (i-awGA) were proposed. Numerical analyses for case study show the effectiveness of proposed approach. Received: June 2005 / Accepted: December 2005     

Neurovision-based logic control of an experimental manufacturing plant using neural net le-net5 and automation Petri nets

In this paper, Petri nets and neural networks are used together in the development of an intelligent logic controller for an experimental manufacturing plant to provide the flexibility and intelligence required from this type of dynamic systems. In the experimental setup, among deformed and good parts to be processed, there are four different part types to be recognised and selected. To distinguish the correct part types, a convolutional neural net le-net5 based on-line image recognition system is established. Then, the necessary information to be used within the logic control system is produced by this on-line image recognition system. Using the information about the correct part types and Automation Petri nets, a logic control system is designed. To convert the resulting Automation Petri net model of the controller into the related ladder logic diagram (LLD), the token passing logic (TPL) method is used. Finally, the implementation of the control logic as an LDD for the real time control of the manufacturing system is accomplished by using a commercial programmable logic controller (PLC).     

A diagnosis scheme for a large-scale system

This paper presents a method for diagnosis in a large-scale system environment. The method utilizes the theory of hierarchical systems and hybrid diagnostic reasoning from AI (artificial intelligence). In shallow reasoning, which is a part of hybrid reasoning, the concept of entropy is used to determine which component (that might be responsible for a symptom observed) is to be tested next. The procedure is illustrated using a simulated example of a CIM (computer-integrated manufacturing) system, and is implemented on IntelliCorp's knowledge engineering environment (KEE).     

Flexible manufacturing cells and systems with computer intelligence

Conditions for the introduction of flexible automated manufacturing systems have much improved. Future flexible manufacturing cells and systems will increasingly use computer intelligence. The paper describes system concepts, the design and function of components, system control and supervisory strategies. The planning and introduction of flexible automated manufacturing systems and their economical application will also be discussed.     

A knowledge-based simulation environment for hierarchical flexiblemanufacturing

This article presents an approach to embedding expert systems within an object oriented simulation environment. The basic idea is to create classes of expert system models that can be interfaced with other model classes. An expert system shell is developed within a knowledge-based design and simulation environment which combines artificial intelligence and systems modeling concepts. In the given framework, interruptible and distributed expert systems can be defined as components of simulations models. This facilitates simulation modeling of knowledge-based controls for flexible manufacturing and many other autonomous intelligent systems. Moreover, the structure of a system can be specified using a recursive system entity structure (SES) and unfolded to generate a family of hierarchical structures using an extension of SES pruning called recursive pruning. This recursive generation of hierarchical structures is especially appropriate for design of multilevel flexible factories. The article illustrates the utility of the proposed framework within the flexible manufacturing context     

Integration and control of intelligence in distributed manufacturing

The area of intelligent systems has generated a considerable amount of interest—occasionally verging on controversy—within both the research community and the industrial sector. This paper aims to present a unified framework for integrating the methods and techniques related to intelligent systems in the context of design and control of modern manufacturing systems. Particular emphasis is placed on the methodologies relevant to distributed processing over the Internet. Following presentation of a spectrum of intelligent techniques, a framework for integrated analysis of these techniques at different levels in the context of intelligent manufacturing systems is discussed. Integration of methods of artificial intelligence is investigated primarily along two dimensions: the manufacturing product life-cycle dimension, and the organizational complexity dimension. It is shown that at different stages of the product life-cycle, different intelligent and knowledge-oriented techniques are used, mainly because of the varied levels of complexity associated with those stages. Distribution of the system architecture or system control is the most important factor in terms of demanding the use of the most up-to-date distributed intelligence technologies. A tool set for web-enabled design of distributed intelligent systems is presented. Finally, the issue of intelligence control is addressed. It is argued that the dominant criterion according to which the level of intelligence is selected in technological tasks is the required precision of the resulting operation, related to the degree of generalization required by the particular task. The control of knowledge in higher-level tasks has to be executed with a strong involvement of the human component in the feedback loop. In order to facilitate the human intervention, there is a need for readily available, user-transparent computing and telecommunications infrastructure. In its final part, the paper discusses currently emerging ubiquitous systems, which combine this type of infrastructure with new intelligent control systems based on a multi-sensory perception of the state of the controlled process and its environment to give us tools to manage information in a way that would be most natural and easy for the human operator.     

SAPT—Knowledge-based CAPP system

The system for automatic programming technology (SAPT system) is a part of the designer-expert system. The new developments in artificial intelligence are promising for design and use knowledge-based expert systems. For a manufacturing process planning knowledge base, the method of knowledge representation and reasoning strategy are given. GT and TT concepts are discussed in detail from a KB approach. Production rules are given for knowledge sources, tasks and meta-knowledge. For two real life, examples, a LISP-program has been written and executed. The results obtained are encouraging and further research is in progress.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

On the Use of Genetic Algorithm for Solving Re-entrant Flowshop Scheduling with Sum-of-processing-times-based Learning Effect to Minimize Total Tardiness

Most research studies on scheduling problems assume that a job visits certain machines only one time. However, this assumption is invalid in some real-life situations. For example, a job may be processed by the same machine more than once in semiconductor wafer manufacturing or in a printed circuit board manufacturing machine. Such a setting is known as the “re-entrant flowshop”. On the other hand, the importance of learning effect present in many practical situations such as machine shop, in different branches of industry and for a variety of corporate activities, in shortening life cycles, and in an increasing diversity of products in the manufacturing environment. Inspired by these observations, this paper addresses a re-entrant m-machine flowshop scheduling problems with time-dependent learning effect to minimize the total tardiness. The complexity of the proposed problem is very difficult. Therefore, in this paper we first present four heuristic algorithms, which are modified from existing algorithms to solve the problem. Then, we use the solutions as four initials to a genetic algorithm. Finally, we report experimental performances of all the proposed methods for the small and big numbers of jobs, respectively     

Modeling rough granular computing based on approximation spaces

The results reported in this paper create a step toward the rough set-based foundations of data mining and machine learning. The approach is based on calculi of approximation spaces. In this paper, we present the summarization and extension of our results obtained since 2003 when we started investigations on foundations of approximation of partially defined concepts (see, e.g., [2], [3], [7], [37], [20], [21], [5], [42], [39], [38], [40]). We discuss some important issues for modeling granular computations aimed at inducing compound granules relevant for solving problems such as approximation of complex concepts or selecting relevant actions (plans) for reaching target goals. The problems discussed in this article are crucial for building computer systems that assist researchers in scientific discoveries in many areas such as biology. In this paper, we present foundations for modeling of granular computations inside of system that is based on granules called approximation spaces. Our approach is based on the rough set approach introduced by Pawlak [24], [25]. Approximation spaces are fundamental granules used in searching for relevant complex granules called as data models, e.g., approximations of complex concepts, functions or relations. In particular, we discuss some issues that are related to generalizations of the approximation space introduced in [33], [34]. We present examples of rough set-based strategies for the extension of approximation spaces from samples of objects onto a whole universe of objects. This makes it possible to present foundations for inducing data models such as approximations of concepts or classifications analogous to the approaches for inducing different types of classifiers known in machine learning and data mining. Searching for relevant approximation spaces and data models are formulated as complex optimization problems. The proposed interactive, granular computing systems should be equipped with efficient heuristics that support searching for (semi-)optimal granules.     

Manufacturing systems: A knowledge- and optimization-based approach

Optimization approaches have traditionally been viewed as tools for solving manufacturing problems. The optimization approach is not suitable for many problems arising in modern manufacturing systems due to their complexity and involvement of qualitative factors. Expert systems appear to remedy the latter weakness of optimization approaches. The biggest disadvantage of expert systems in manufacturing environment is the slow response time. In this paper an integrand approach involving knowledge-based and optimization approaches is explored. The knowledge-and optimization-based approach is applied to solve two manufacturing problems: group technology (static problem) and scheduling (dynamic problem). The approach presented is illustrated with numerical example and computational results.The original version of this paper was presented at the 2nd International Symposium on Robotics and Manufacturing (ISRAM), Albuquerque, New Mexico 16–18 November 1988. The published proceedings of this meeting may be ordered from: CAD Laboratory for Systems/Robotics, EECE Dept, UNM, Albuquerque, NM 87131, U.S.A.     

Visionary prototyping

Today, besides introducing intelligence directly into equipment/systems through embedded microcomputers and providing virtual prototyping through enhanced computer-aided design/computer-aided engineering (CAD/CAE) facilities, information now is well regarded as an essential part of the integrated design approach whereby all members of the prototype development and manufacturing automation team can work closely together throughout the design and manufacturing cycle. The article focuses on two subtopics. The first is the development of a theory for prototyping discrete-event and hybrid systems and its applications. In discrete-event dynamic systems (DEDS), state transitions are caused by internal, discrete events in the system. An overview for the development of a simple graphical environment for simulating, analyzing, synthesizing, monitoring, and controlling discrete-event and hybrid systems is also presented. The second focus is on prototyping machine vision for real-time automation applications. We discuss the problems associated with traditional machine vision systems for cost-effective, real-time applications, novel alternative system design to overcome these problems, and the new trends of modern vision sensors. Modern smart sensors provide the features of traditional machine vision systems at less than half of the usual price by eliminating the signal-conversion electronics, fixed-frame rates, and limited gray-scale quantization. The camera, image-acquisition electronics, and computer are integrated into a single unit to allow dynamic access to the charge-coupled devices without image float or flutter. We also present a physically accurate image synthesis method as a flexible, practical tool for examining a large number of hardware/software configuration combinations for a wide range of parts     

制造系统生态化概念、 内涵及展望

当前我国传统制造业受到了多重压力,如何实现突围是我国经济发展的难题.从整体上看,我国全产业链全要素在整体上完备充足,但从局部上,很多传统产品制造系统链条过长,在面临不确定时出现效率不高和能力不足问题,如何将整体充足的要素资源和闲置的能力整合服务于面临困境的制造体系值得研究.商业生态系统是一种多边合作机制,在汇聚资源、促进跨领域多资源的互动和协同具有优势,作为一种复杂条件下协调跨组织关系的治理机制对于解决传统制造企业面临的困境具有重要意义.然而,实际中制造企业受传统链式模式思维影响,对于如何借助商业生态系统模式解决企业发展中的问题较为模糊.理论领域对商业生态系统已开展研究,但多是针对纯商业生态系统模式展开讨论,针对传统制造系统融合商业生态系统模式(即制造系统生态化)的研究尚处于起步阶段.鉴于此,通过相关概念梳理、文献综述总结制造系统生态化的概念及内涵,并对制造系统生态化的未来研究提出展望.     

A modeling technique for loading and scheduling problems in FMS

In recent years, due to highly competitive market conditions, it has become necessary for manufacturing systems to have quick response times and high flexibility. Flexible manufacturing systems (FMS's) have gained attention in response to this challenge. FMS has the ability to produce a variety of parts using the same system. However this flexibility comes at the price, which is the development of efficient and effective methods for integrated production planning, and control.In this paper, we analyze the production planning problem in flexible manufacturing systems. We address the problems of part loading, tool loading, and part scheduling. We assume that there is a set of tools with known life and a set of machines that can produce a variety of parts. A batch of various part types is routed through this system with the assumption that the processing time and cost vary with the assignment of parts to different machines and assignment of various tool sets to machines. We developed a mathematical model to select machines and assign operations and the required tools to machines in order to minimize the summation of maximum completion time, material handling time, and total processing time.We first integrate and formulate loading, and routing, two of the most important FMS planning problems, as a 0–1 mixed integer programming problem. We then take the output from the integrated planning model and generate a detailed operations schedule. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as production rate and utilization.