A transformation from a Boolean equation control specification to a Petri net

In this article we present an algorithm to transform a set(s) of Boolean equations into a Petri net(s). Boolean logic provides a mathematical basis for switching control and many other areas. In the case of programmable logic controllers used for the sequential control in manufacturing applications, Boolean equations are fundamental both in programming and in understanding their operation. A major difficulty with Boolean equations is the lack of any type of mathematical theory for the analysis of the systems that are being represented by these equations. Petri nets, on the other hand, possess a mathematical framework to analyze the properties of the system being modeled. Furthermore, Petri nets have been proved to be appropriate models for sequential control. The popularity as well as the generality of Boolean logic, on one hand, and the modeling and analytical capabilities of Petri nets, on the other hand, are the major motivations for this research.     

A hybrid shop-floor control system for food manufacturing

This paper describes research in the area of hybrid control using simulation including a suitable architecture and a test-bed developed for experimenting with hybrid manufacturing systems - that is, manufacturing systems containing both continuous and discrete processing activities. The paper builds on the RapidCIM control system developed at Penn State University and makes innovations in this work including, integrating continuous simulation and discrete event simulation into a message-based process control system. Simulation technology is extended to support real-time communication to access remote databases, and pass messages, which are used to control hardware equipment performing both continuous processes and discrete activities. This makes possible the development of a generic hybrid control capability. In RapidCIM, software is automatically generated to control and coordinate the physical system, This can substantially reduce the cost of developing and integrating such systems, and allows a detailed simulation to be used for both analysis as well as for control. An experimental research prototype of such a hybrid control system has been constructed for the Pennsylvania State University Creamery, in which unit processes and operational decisions are integrated.     

Modelling and simulation of manufacturing systems with first-order hybrid Petri nets

First-order hybrid Petri nets are models that consist of continuous places holding fluid, discrete places containing a non-negative integer number of tokens, and transitions, either discrete or continuous. In the first part of the paper, we provide a framework to describe the overall hybrid net behaviour that combines both time-driven and event-driven dynamics. The resulting model is a linear discrete-time, time-varying state variable model that can be directly used by an efficient simulation tool. In the second part of the paper, we focus on manufacturing systems. Manufacturing systems are discrete-event dynamic systems whose number of reachable states is typically very large, hence approximating fluid models have often been used in this context. We describe the net models of the elementary components of a flexible manufacturing system (machines and buffers) and we show in a final example how these modules can be put together in a bottom-up fashion.     

Modelling hybrid production systems through the ACD specification: a case study in the fibre-glass industry

This paper is aimed at formalising a model for hybrid production systems where the interactions between the continuous process parts and the manufacturing sub-systems are given by minor stoppages. The proposal is to represent the effects of the continuous process dynamics on discrete manufacturing sub-systems using autoregressive conditional duration (ACD) models originally conceived to treat high-frequency and irregularly spaced financial transaction data. The proposed methodology has been tested on a real-life fibre-glass production plant exploiting simulation techniques. The physical model of the furnace and spooling-bushing department has been run in two different conditions: (i) the fibre-glass breakings, i.e. minor stoppages, on the spooling-bushing machines are generated according to the proposed approach; (ii) historical fibre-glass breakings data are used. The comparison between the simulation results shows that for three spooling-bushing machines only out of 24, the daily throughput of completed spools and the empirical distributions for the uncompleted spools weight are dissimilar under the two different conditions. Therefore, the ACD-based model proved useful for representing the occurrence of fibre-glass breakings on the spooling-bushing machines and, in more general terms, for the logical modelling of the hybrid production systems, where the relationships between continuous and discrete parts are given by minor stoppages.     

On the dynamics of inventory control systems

This paper eals with the dynamics of a computer-based inventory control system. In general Buch a real-life system is designed according to the most basic principles of static inventory theory. To control the system in a dynamic sense, management has to vary the policy parameters of the system. Two different mathematical models of inventory dynamics are introduced. The first ie a simple first-order differential equation and the second a discrete multidimensional model. Applications of the two models are discussed with the aid of examples concerning, on the one hand, changes in policy parameters, and on the other production smoothing.     

To react or not to react? Intrinsic stochasticity of human control in virtual stick balancing

Understanding how humans control unstable systems is central to many research problems, with applications ranging from quiet standing to aircraft landing. Increasingly, much evidence appears in favour of event-driven control hypothesis: human operators only start actively controlling the system when the discrepancy between the current and desired system states becomes large enough. The event-driven models based on the concept of threshold can explain many features of the experimentally observed dynamics. However, much still remains unclear about the dynamics of human-controlled systems, which likely indicates that humans use more intricate control mechanisms. This paper argues that control activation in humans may be not threshold-driven, but instead intrinsically stochastic, noise-driven. Specifically, we suggest that control activation stems from stochastic interplay between the operator''s need to keep the controlled system near the goal state, on the one hand, and the tendency to postpone interrupting the system dynamics, on the other hand. We propose a model capturing this interplay and show that it matches the experimental data on human balancing of virtual overdamped stick. Our results illuminate that the noise-driven activation mechanism plays a crucial role at least in the considered task, and, hypothetically, in a broad range of human-controlled processes.     

An interdisciplinary course in engineering synthesis

The interdisciplinary courseSynthesis of Engineering Systems has been offered by the Engineering Design Research Center over the last 3 years. Students are exposed to two major paradigms for design synthesis: mathematical programming and knowledge-based expert systems. The former emphasizes the mathematical formulation of optimization problems that involve discrete and continuous variables for the selection of topologies and parameters in engineering systems. The latter emphasizes representations and search techniques for processing qualitative knowledge for synthesis of designs by heuristic classification and hierarchical decomposition.An integral part of the course is a term project in which the students apply both synthesis approaches to a design problem in their domain. The projects deal with different engineering problems, reflecting the disciplinary background of the students. The projects explore a number of different schemes for combining mathematical optimization and knowledge-based approaches. Some use knowledge-based techniques for preliminary screening or as critics of mathematical optimization, while others show a direct comparison between the two approaches. This paper summarizes the experience gained in the course, illustrates representative student projects, discusses major results and conclusions, and provides a perspective for future research needs and educational approaches in this area.     

Computerized investigation of robust measurement systems

This paper deals with the development of software for investigating the robust properties of measurement systems and for their design and tuning in order to improve their robustness. The software constitutes Simulink models and m-files as extensions of the libraries of MATLAB. The investigations on continuous measurement systems (a self-balancing system) and discrete systems (ADCs) with improved robustness by using the internal model controller technique revealed new properties-fast dynamics, high accuracy, and discretization error reduction via multiple measurements     

Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis

During the last years, the impacts caused by digital transformation on companies have been disruptive. Contrarily to prior technological revolutions, the current scenario is characterized by the rapid growth of innovation that has impacted organizations differently. In particular, an increasing number of organizations revised their management control systems to adequate their business models to the external pressures made by competitors and regulators. The research aims consist of a bibliometric analysis about the impacts caused by digital transformation on managerial auditing. The research reveals the existence of four independent research area: continuous auditing (Green Cluster), fraud detection (Blue Cluster), data analytics (Yellow Cluster) and technological innovation (Red Cluster). Finally, we developed a research agenda in order to address future research.     

An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles

With the growing number of applications of artificial intelligence such as autonomous cars or smart industrial equipment, the inaccuracy of utilized machine learning algorithms could lead to catastrophic outcomes. Human-in-the-loop computing combines human and machine intelligence resulting in a hybrid intelligence of complementary strengths. Whereas machines are unbeatable in logic and computation speed, humans are contributing with their creative and dynamic minds. Hybrid intelligent systems are necessary to achieve high accuracy and reliability of machine learning algorithms. In a design science research project with a Swedish manufacturing company, this paper presents an application of human-in-the-loop computing to make operational processes more efficient. While conceptualizing a Smart Power Distribution for electric industrial equipment, this research presents a set of principles to design machine-learning algorithms for hybrid intelligence. From being AI-ready as an organization to clearly focusing on the customer benefits of a hybrid intelligent system, designers need to build and strengthen the trust in the human-AI relationship to make future applications successful and reliable. With the growing trends of technological advancements and incorporation of artificial intelligence in more and more applications, the alliance of humans and machines have become even more crucial.     

Design of predictable production scheduling model using control theoretic approach

As one of the most important planning and operational issues in manufacturing systems, production scheduling generally deals with allocating a set of resources over time to perform a set of tasks. Recently, control theoretic approaches based on nonlinear dynamics of continuous variables have been proposed to solve production scheduling problems as an alternative to traditional production scheduling methods that deal with decision-making components in discrete nature. The major goal of this paper is to improve predictability and performance of an existing scheduling model that employs the control theoretic approach, called distributed arrival time controller (DATC), to manage arrival times of parts using an integral controller. In this paper, we first review and investigate unique dynamic characteristics of the DATC in regards to convergence and chattering of arrival times. We then propose a new arrival time controller for the DATC that can improve predictability and performance in production scheduling. We call the new mechanism the double integral arrival-time controller (DIAC). We analyse unique characteristics of the DIAC such as oscillatory trajectory of arrival times, their oscillation frequency, and sequence visiting mechanism. In addition, we compare scheduling performance of the DIAC to the existing DATC model through computational experiments. The results show that the proposed system can be used as a mathematical and simulation model for designing adaptable manufacturing systems in the future.     

计算多体系统动力学中引入多体系统离散时间传递矩阵法的混合算法研究

以复杂机械系统动力学建模为研究背景,在计算多体动力学和多体系统离散时间传递矩阵法的理论基础上,论述了这两种方法动力学建模与分析的特点;在此基础上,提出了在计算多体动力学中混合多体系统离散时间传递矩阵法的算法,并论述了使用该算法对机械系统进行动力学建模及分析的过程。该算法适合于对复杂机械系统进行动力学建模及分析,并且易于使用计算机应用软件实现。以某典型机械连接结构为算例,通过该算法进行建模与分析,论证了该算法的可行性,也表明该算法的优点。     

技术设计的哲学思想溯源

目的 设计是人类创造技术人工物的实践活动形式，技术设计概念的提出为设计研究奠定了重要的哲学根基，本文旨在探究技术设计的哲学思想之源，以期从技术哲学范畴启迪设计科学研究和设计实践创新。方法 研究溯源技术哲学的发端、经典理论及两次经验转向脉络，探究支撑技术设计研究的现象学技术哲学思想基础，并相应从实用主义和实证主义出发，分析和论证技术哲学思想对设计研究进路的影响。结论 阐明了技术设计的哲学思想基础和演进脉络，发展了技术设计理论的实践情境研究进路与理性逻辑哲学研究进路，并证明了两条技术设计研究进路在哲学思想和逻辑上存在一定的同源性与互应性。     

Scheduling in production,supply chain and Industry 4.0 systems by optimal control: fundamentals,state-of-the-art and applications

This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal–logical constraints with applications to short term job and flow shop scheduling, and hybrid structural–terminal–logical constraints with applications to customised assembly systems such as Industry 4.0. Computational algorithms in state, control and adjoint variable spaces are discussed.     

Production control and combined discrete/continuous simulation modeling in failure-prone transfer lines

In this paper, we consider a production system consisting of multiple tandem machines subject to random failures. The objective of the study is to find the production rates of the machines in order to minimize the total inventory and backlog costs. By combining analytical formalism and simulation-based statistical tools such as design of experiments (DOE) and response surface methodology (RSM), an approximation of the optimal control policy is obtained. The combined discrete/continuous simulation modeling is used to obtain an estimate of the cost in a fraction of the time necessary for discrete event simulation by reducing the number of events related to parts production. This is achieved by replacing the discrete dynamics of part production by a set of differential equations that describe this process. This technique makes it possible to tackle optimization problems that would otherwise be too time consuming. We provide some numerical examples of optimization and compare computational times between discrete event and discrete/continuous simulation modeling. The proposed combination of DOE, RSM and combined discrete/continuous simulation modeling allows us to obtain the optimization results in a fairly short time period on widely available computer resources.     

A Novel Design of Octal-Valued Logic Full Adder Using Light Color State Model

Due to the demand of high computational speed for processing big data that requires complex data manipulations in a timely manner, the need for extending classical logic to construct new multi-valued optical models becomes a challenging and promising research area. This paper establishes a novel octal-valued logic design model with new optical gates construction based on the hypothesis of Light Color State Model to provide an efficient solution to the limitations of computational processing inherent in the electronics computing. We provide new mathematical definitions for both of the binary OR function and the PLUS operation in multi valued logic that is used as the basis of novel construction for the optical full adder model. Four case studies were used to assure the validity of the proposed adder. These cases proved that the proposed optical 8-valued logic models provide significantly more information to be packed within a single bit and therefore the abilities of data representation and processing is increased.     

Making IT fit: the design of integrated manufacturing systems

One of the important implications of information technology (IT) is that it has made possible widespread automation in manufacturing industry. However, the bulk of this has involved discrete automation of single plant items or process elements rather than the total production system. Although the capital cost of such total system automation is a significant factor in slowing the rate of change in this direction, there are also a number of problem issues related to the integration of machines, computers and human systems within manufacture and design. Arguably the types of issue involved are not fully understood and in many cases the technological change is demanding totally new approaches and responses to the design of production systems and the traditional manufacture/design connections.The paper explores some of the issues raised in advanced manufacturing systems, drawing on case study research into the adoption of flexible manufacturing systems as an example of computer-integrated manufacture. It comments on the experience of case study firms and presents some comments on the design problems facing those responsible for integrated manufacturing systems.     

Modelling excitable cells using cycle-linear hybrid automata

Cycle-linear hybrid automata (CLHAs), a new model of excitable cells that efficiently and accurately captures action-potential morphology and other typical excitable-cell characteristics such as refractoriness and restitution, is introduced. Hybrid automata combine discrete transition graphs with continuous dynamics and emerge in a natural way during the (piecewise) approximation process of any nonlinear system. CLHAs are a new form of hybrid automata that exhibit linear behaviour on a per-cycle basis but whose overall behaviour is appropriately nonlinear. To motivate the need for this modelling formalism, first it is shown how to recast two recently proposed models of excitable cells as hybrid automata: the piecewise-linear model of Biktashev and the nonlinear model of Fenton-Karma. Both of these models were designed to efficiently approximate excitable-cell behaviour. We then show that the CLHA closely mimics the behaviour of several classical highly nonlinear models of excitable cells, thereby retaining the simplicity of Biktashev's model without sacrificing the expressiveness of Fenton-Karma. CLHAs are not restricted to excitable cells; they can be used to capture the behaviour of a wide class of dynamic systems that exhibit some level of periodicity plus adaptation.     

Structure dynamics control approach to supply chain planning and adaptation

Many of the existing methods and models of supply chain planning and control consider only one supply chain structure and assume it's more or less static in nature. In practice, supply chains have a multi-structural semantics and those multiple interrelated structures (organisational, functional, informational, financial, topological, technological, product, and energy structures) are dynamic and subject to many planned and disturbance-based changes. These changes are tackled by control activities which make planning an adaptive process. Although the supply chain optimal planning domain for static structures has been extensively investigated, the domain of planning and control of supply chains with structure dynamics merits more systematic and critical attention. In this study, an original form of supply chain representation as a dynamic system with changing multi-structural characteristics is developed. The structure dynamics control is a dynamic interpretation of the supply chain (re)synthesis process and aims at both advancing the supply chain (re)planning domain and enlarging the scope of the supply chain analysis domain that is currently rather limited. The developed approach is based on an optimal program control theory, active modelling objects, and structural–mathematical concept blended with mathematical programming. In this paper, we describe its basic features and exemplify possible applications and extensions regarding real planning and control problems faced by international supply chains in recent years.