ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Towards smart factory for industry 4.0

This paper explores the role of Internet of Things (IoT) and its impact on supply chain management (SCM) through an extensive literature review. Important aspects of IoT in SCM are covered including IoT definition, main IoT technology enablers and various SCM processes and applications. We offer several categorisation of the extant literature, such as based on methodology, industry sector and focus on a classification based on major supply chain processes. In addition, a bibliometric analysis of the literature is also presented. We find that most studies have focused on conceptualising the impact of IoT with limited analytical models and empirical studies. In addition, most studies have focused on the delivery supply chain process and the food and manufacturing supply chains. Areas of future SCM research that can support IoT implementation are also identified.

Internet of things and supply chain management: a literature review

An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi’s experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.

Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

Deadlocks are a rather undesirable situation in a highly automated flexible manufacturing system. Their occurrences often deteriorate the utilization of resources and may lead to catastrophic results in safety-critical systems. Graph theory, automata, and Petri nets are three important mathematical tools to handle deadlock problems in resource allocation systems. Particularly, Petri nets are considered as a popular formalism because of their inherent characteristics. They received much attention over the past decades to deal with deadlock problems, leading to a variety of deadlock-control policies. This study surveys the state-of-the-art deadlock-control strategies for automated manufacturing systems by reviewing the principles and techniques that are involved in preventing, avoiding, and detecting deadlocks. The focus is deadlock prevention due to its large and continuing stream of efforts. A control strategy is evaluated in terms of computational complexity, behavioral permissiveness, and structural complexity of its deadlock-free supervisor. This study provides readers with a conglomeration of the updated results in this area and facilitates engineers in finding a suitable approach for their industrial scenarios. Future research directions are finally discussed.

Deadlock Control of Automated Manufacturing Systems Based on Petri Nets—A Literature Review

Over the last two decades, a great deal of research has been focused on solving deadlock problems in resource allocation systems such as computer communication systems, workflow systems, and flexible manufacturing systems, resulting in a wide variety of approaches. As a well-defined problem in resource allocation systems, deadlock prevention based on a Petri net formalism has received an enormous amount of attention in the literature. This paper intends to review and compare a variety of Petri net-based deadlock prevention policies reported in the literature. Their comparison is done in terms of structural complexity, behavior permissiveness, and computational complexity. This paper should facilitate engineers in choosing a suited method for their industrial application cases.

A Survey and Comparison of Petri Net-Based Deadlock Prevention Policies for Flexible Manufacturing Systems

With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.

Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm

Petri nets are widely used in modeling, analysis, and control of discrete event systems arising from manufacturing, transportation, computer and communication networks, and web service systems. However, Petri net models for practical systems can be very large, making it difficult to apply such models to real-life problems. System Modeling and Control with Resource-Oriented Petri Nets introduces a new resource-oriented Petri net (ROPN) model that was developed by the authors. Not only does it successfully reduce model size, but it also offers improvements that facilitate effective modeling, analysis, and control of automated and reconfigurable manufacturing systems. Presenting the latest research in this novel approach, this cutting-edge volume provides proven theories and methodologies for implementing cost and time-saving improvements to contemporary manufacturing systems. It provides effective tools for deadlock avoidancedeadlock-free routing and deadlock-free scheduling. The authors supply simple and complex industrial manufacturing system examples to illustrate time-tested concepts, theories, and approaches for solving real-life application problems. Written in a clear and concise manner, the text covers applications to automated and reconfigurable manufacturing systems, automated guided vehicle (AGV) systems, semiconductor manufacturing systems, and flexible assembly systems. Explaining complex concepts in a manner that is easy to understand, the authors provide the understanding and tools needed for more effective modeling, analysis, performance evaluation, control, and scheduling of engineering processes that will lead to more flexible and efficient manufacturing systems.

System Modeling and Control with Resource-Oriented Petri Nets

In many flexible assembly systems, base components are transported with pallets; parts to be mounted onto the base ones are transported by trays with no pallets. When an assembly operation is performed by using some parts in a tray but not all, the tray with the remaining parts still occupies a buffer space. In this way, an assembly/disassembly material flow is formed. In such a material flow, deadlock can occur both in the base component and part flow. Furthermore, the assembly operations can also result in a deadlock. Thus, it is a great challenge to tackle deadlocks in such processes. This paper models them using resource-oriented Petri nets. Based on the models, a deadlock control policy is proposed and proved to be computationally efficient and less conservative than the existing policies in the literature. An industrial case study is used to show the results.

Naiqi Wu

Papers

Deadlock Control of Automated Manufacturing Systems Based on Petri Nets—A Literature Review

A Survey and Comparison of Petri Net-Based Deadlock Prevention Policies for Flexible Manufacturing Systems

Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm

System Modeling and Control with Resource-Oriented Petri Nets

Resource-Oriented Petri Net for Deadlock Avoidance in Flexible Assembly Systems